emergenCITY Policy Paper Series

In our view, there is an urgent need to increase and sustain the resilience of current and future information and communication technology (ICT). We call ICT resilient if it can maintain an acceptable minimum or substitute functionality despite significant impairments and is equipped for a swift return to normal behaviour.
Also available at emergencity.de/s/pp1.

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The system of our critical infrastructures is becoming more complex and crisis-prone. Human or technical failure, natural disasters, pandemics, cyber or terrorist attacks can also lead to a supraregional power blackout in Germany that lasts longer than 24 hours.
Also available at emergencity.de/s/pp2.

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Many emergency forces are involved in disaster operations of the magnitude of the Ahr Valley flood. Coordination between these units is one of the major challenges. The Policy Paper analyzes the weak points and provides recommendations for action to improve disaster management.
Also available at emergencity.de/s/pp3.

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Successful urban disaster management requires cooperation of public authorities, security organizations, private companies and citizens. Participation plays a central role, as early involvement of stakeholders increases the quality and acceptance of decisions. In this context, co-creation processes are crucial for enhancing urban resilience and managing crises.
Also available at emergencity.de/s/pp4.

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The dependencies of agriculture on communication and energy infrastructure are increasing. With continuous digitalization and the growing importance of data, security risks must be critically examined. Enhanced security and resilience against infrastructure failures, as well as data protection, are essential. Developed with foresight, digitalization can improve the resilience and efficiency of agriculture.
Also available at emergencity.de/s/pp5.

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Scientific Publications

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December 2024 Results in Engineering Article

Integrated risk analysis for urban flooding under changing climates

Wenyu Yang, Ziyong Zhao, Liping Pan, Ruifei Li, Shixue Wu, Pei Hua, Haijun Wang, Britta Schmalz, Peter Krebs, Jin Zhang

BibTeX DOI: 10.1016/j.rineng.2024.103243

Abstract

Urban flooding poses significant threats to human lives and urban development worldwide, while the impact of climate change on urban flooding remains unclear. To systematically analyze the variabilities of hydrological patterns and urban flooding under changing climates, this study coupled the downscaled general circulation model (GCM) projection with hydrologic-hydraulic modeling, with consideration of both the low-greenhouse gas (GHG) emission scenario of SSP1–2.6 and the high-GHG emission scenario of SSP5–8.5. Results demonstrated that the original GCM projection effectively captured the changing trend of rainfall patterns in the given area, with an overestimation of rainfall peaks. The GCM downscaling through the k-nearest neighbors (kNN)-based analog method significantly improved the model accuracy. Scenario analysis indicated that climate change significantly affected the regional hydrology, with the precipitation, surface runoff, and floods increasing by a maximum value of 17.10%, 12.66%, and 63.26%, respectively. The interannual comparison demonstrated that the temporal variability in precipitation and flood intensified with the increase in GHG emissions during 2025–2100, suggesting the uncertainty of long-term climate forecasts. According to flood risk analysis, long-term and short-term floods exhibit varied changing trends across climate scenarios, despite the strong correlations between precipitation and runoff, implying the complexity of flood generation mechanisms. The methods and findings herein provide valuable insights for policymakers and practitioners, to cope with the increasing urban flood risk within the context of evolving environment.

November 2024 Technische Universität Darmstadt Darmstadt Thesis

Development of Fast Machine Learning Algorithms for False Discovery Rate Control in Large-Scale High-Dimensional Data

Jasin Machkour

PDF BibTeX DOI: 10.26083/tuprints-00028231

Abstract

This dissertation develops false discovery rate (FDR) controlling machine learning algorithms for large-scale high-dimensional data. Ensuring the reproducibility of discoveries based on high-dimensional data is pivotal in numerous applications. The developed algorithms perform fast variable selection tasks in large-scale high-dimensional settings where the number of variables may be much larger than the number of samples. This includes large-scale data with up to millions of variables such as genome-wide association studies (GWAS). Theoretical finite sample FDR-control guarantees based on martingale theory have been established proving the trustworthiness of the developed methods. The practical open-source R software packages TRexSelector and tlars, which implement the proposed algorithms, have been published on the Comprehensive R Archive Network (CRAN). Extensive numerical experiments and real-world problems in biomedical and financial engineering demonstrate the performance in challenging use-cases. The first three main parts of this dissertation present the methodological and theoretical contributions, while the fourth main part contains the practical contributions. The first main part (Chapter 3) is dedicated to the Terminating-Random Experiments (T-Rex) selector, a new fast variable selection framework for high-dimensional data. The proposed T-Rex selector controls a user-defined target FDR while maximizing the number of selected variables. This is achieved by fusing the solutions of multiple early terminated random experiments. The experiments are conducted on a combination of the candidate variables and multiple independent sets of randomly generated dummy variables. A finite sample proof of the FDR control property is provided using martingale theory. The computational complexity of the T-Rex selector grows linearly with the number of candidate variables. Furthermore, its computation time is more than two orders of magnitude faster compared to state-of-the-art benchmark methods in large-scale data settings. Therefore, the T-Rex selector scales to millions of candidate variables in a reasonable computation time. An important use-case of the T-Rex selector is determining reproducible associations between phenotypes and genotypes in GWAS, which is imperative in personalized medicine and drug discovery. The second main part (Chapter 4) concerns dependency-aware FDR-controlling algorithms for large-scale high-dimensional data. In many biomedical and financial applications, the high-dimensional data sets often contain highly correlated candidate variables (e.g., gene expression data and stock returns). For such applications, the dependency-aware T-Rex (T-Rex+DA) framework has been developed. It extends the ordinary T-Rex framework by accounting for dependency structures among the candidate variables. This is achieved by integrating graphical models within the T-Rex framework, which allows to effectively harness the dependency structure among variables and to develop variable penalization mechanisms that guarantee FDR control. In the third main part (Chapter 5), algorithms for joint grouped variable selection and FDR control are proposed. This approach to tackling the challenges resulting from the presence of groups of highly dependent variables in the data is different to the more conservative variable penalization approach that has been developed in the second part of this dissertation. That is, instead of finding the few true active variables among groups of highly correlated variables, the goal is to select all groups of highly correlated variables that contain at least one true active variable. In genomics research, especially for GWAS, grouped variable selection approaches are highly relevant, since one is not interested in identifying a few single-nucleotide polymorphisms (SNPs) that are associated with a disease of interest but rather the entire groups of correlated SNPs that point to relevant locations on the genome. The fourth main part of this dissertation (Chapters 6 and 7) demonstrates the application of the developed methods to practical problems in biomedical engineering as well as financial engineering. The biomedical applications include (i) a semi-real-world GWAS, (ii) a human immunodeficiency virus type 1 (HIV-1) data set with associated drug resistance measurements, and (iii) a breast cancer data set with associated survival times of the patients. The financial engineering applications include (i) accurately tracking the S&P 500 index using a quarterly updated and rebalanced tracking portfolio that consists of few stocks and (ii) a factor analysis of S&P 500 stock returns. The common challenge of all considered applications lies in detecting the few true active variables (i.e., SNPs, mutations, genes, stocks) among many non-active variables in, among other things, large-scale high-dimensional settings. Summarizing, this dissertation develops and analyses new fast and scalable machine learning algorithms with provable FDR-control guarantees for variable selection tasks in large-scale high-dimensional data. The developed algorithms and the associated open-source software packages have enabled making reproducible discoveries in various real-world applications ranging from biomedical to financial engineering.

November 2024 27th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2024) Conference

PairSonic: Helping Groups Securely Exchange Contact Information

Florentin Putz, Steffen Haesler, Thomas Völkl, Maximilian Gehring, Nils Rollshausen, Matthias Hollick

PDF BibTeX DOI: 10.1145/3678884.3681818

Abstract

Securely exchanging contact information is essential for establishing trustworthy communication channels that facilitate effective online collaboration. However, current methods are neither user-friendly nor scalable for large groups of users. In response, we introduce PairSonic, a novel group pairing protocol that extends trust from physical encounters to online communication. PairSonic simplifies the pairing process by automating the tedious verification tasks of previous methods through an acoustic out-of-band channel using smartphones’ built-in hardware. Our protocol not only facilitates connecting users for computer-supported collaboration, but also provides a more user-friendly and scalable solution to the authentication ceremonies currently used in end-to-end encrypted messengers like Signal or WhatsApp. PairSonic is available as open-source software: https://github.com/seemoo-lab/pairsonic

November 2024 Proceedings of the ACM on Human-Computer Interaction Article

Sounds Good? Fast and Secure Contact Exchange in Groups

Florentin Putz, Steffen Haesler, Matthias Hollick

PDF BibTeX DOI: 10.1145/3686964

Abstract

Trustworthy digital communication requires the secure exchange of contact information, but current approaches lack usability and scalability for larger groups of users. We evaluate the usability of two secure contact exchange systems: the current state of the art, SafeSlinger, and our newly designed protocol, PairSonic, which extends trust from physical encounters to spontaneous online communication. Our lab study (N=45) demonstrates PairSonic’s superior usability, automating the tedious verification tasks from previous approaches via an acoustic out-of-band channel. Although participants significantly preferred our system, minimizing user effort surprisingly decreased the perceived security for some users, who associated security with complexity. We discuss user perceptions of the different protocol components and identify remaining usability barriers for CSCW application scenarios.

November 2024 IEEE Access Article

Delay-Aware Online Resource Allocation for Buffer-Aided Synchronous Federated Learning Over Wireless Networks

Jing Liu, Jing Zheng, Jing Zhang, Lin Xiang, Derrick Wing Kwan Ng, Xiaohu Ge

BibTeX DOI: 10.1109/ACCESS.2024.3489657

Abstract

Synchronous federated learning (FL) over wireless networks often suffers from the straggler effect, when the time required for training local models and uploading trained parameters varies significantly across heterogeneous wireless devices. This disparity prolongs the duration needed for model aggregation at the data center and slows down the convergence of synchronous FL, posing a significant challenge for FL over wireless networks. In this paper, we propose a novel buffer-aided FL scheme to mitigate the straggler effect. A buffer with sufficiently large storage is deployed at each wireless device to temporarily store the collected training data and adaptively outputs it during local training, according to the computational capabilities and communication data rates of the wireless devices. Consequently, all local models can be synchronously aggregated at the data center to reduce the number of rounds required for model aggregation in FL. To ensure timely information updates, a staleness function is further introduced to characterize the freshness of the data used to train local models. Additionally, the entropic value-at-risk (EVaR) of the data queues is introduced to eliminate the impact of discarded data at the buffers and improve the accuracy of trained local models. We formulate a delay-aware online stochastic optimization problem to minimize the long-term average staleness of all wireless devices for buffer-aided FL. Our problem formulation simultaneously guarantees the stability of data queues at the wireless devices and reduces the risk of data loss. By employing the Lyapunov optimization technique, we transform the problem into instantaneous deterministic optimization subproblems and further solve each subproblem online via utilizing its hidden convexity. Simulation results demonstrate that the proposed buffer-aided synchronous FL scheme can effectively improve the convergence rate of FL and, at the same time, ensure timely synchronization of heterogeneous wireless devices.

October 2024 2024 IEEE Conference on Communications and Network Security (CNS) Conference

A Data-Driven Evaluation of the Current Security State of Android Devices

Ernst Leierzopf, René Mayrhofer, Michael Roland, Wolfgang Studier, Lawrence Dean, Martin Seiffert, Florentin Putz, Lucas Becker, Daniel Thomas

BibTeX DOI: 10.1109/CNS62487.2024.10735682

Abstract

Android’s fast-paced development cycles and the large number of devices from different manufacturers do not allow for an easy comparison between different devices’ security and privacy postures. Manufacturers each adapt and update their respective firmware images. Furthermore, images published on OEM websites do not necessarily match those installed in the field. Relevant software aspects do not remain static after initial device release, but need to be measured on real devices that receive these updates. There are various potential sources for collecting such attributes, including webscraping, crowdsourcing, and dedicated device farms. However, raw data alone is not helpful in making meaningful decisions on device security and privacy. We make a website available to access collected data. Our implementation focuses on reproducible requests and supports filtering by OEMs, devices, device models, and attributes. To improve usability, we further propose a security score grounded on the list of attributes. Based on input from Android experts, including a focus group and eight individuals, we have created a method that derives attribute weights from the importance of attributes for mitigating threats on the Android platform. We derive weights for general use cases and suggest possible examples for more specialized weights for groups of confidentiality/privacy-sensitive users and integrity-sensitive users. Since there is no one-size-fits-all setting for Android devices, our website provides the possibility to adapt all parameters of the calculated security score to individual needs.

October 2024 Technische Universität Darmstadt Darmstadt Thesis

Large-Scale Multi-Agent Reinforcement Learning via Mean Field Games

Kai Cui

PDF BibTeX DOI: 10.26083/tuprints-00028568

Abstract

In this dissertation, we discuss the mathematically rigorous multi-agent reinforcement learning frameworks of mean field games (MFG) and mean field control (MFC). Dynamical multi-agent control problems and their game-theoretic counterparts find many applications in practice, but can be difficult to scale to many agents. MFGs and MFC allow the tractable modeling of large-scale dynamical multi-agent control and game problems. In essence, the idea is to reduce interaction between infinitely many homogeneous agents to their anonymous distribution – the so-called mean field. This reduces many practical problems to considering a single representative agent and – by the law of large numbers – its probability law. In this thesis, we present various novel learning algorithms and theoretical frameworks of MFGs and MFC. We address existing algorithmic limitations, and also extend MFGs and MFC beyond their restriction to (i) weakly-interacting agents, (ii) all-knowing and rational agents, or (iii) homogeneity of agents. Lastly, some practical applications are briefly considered to demonstrate the usefulness of our developed algorithms. Firstly, we consider the competitive case of MFGs. There, we show that in the simplest case of finite MFGs, existing algorithms are strongly limited in their generality. In particular, the common assumption of contractive fixed-point operators is shown to be difficult to fulfill. We then contribute and analyze approximate learning algorithms for MFGs based on regularization, which allows for a trade-off between approximation and tractability. We then proceed to extend results to MFGs on graphs and hypergraphs, in order to increase the descriptiveness of MFGs and ameliorate the restriction of homogeneity. Lastly, we also extend towards the presence of both strongly interacting and many weakly-interacting agents, in order to obtain tractability for cases where some agents do not fall under the mean field approximation. Secondly, we investigate cooperative MFC. Initially, we consider an extension to environmental states under a simplifying assumption of static mean fields. Approximate optimality of an MFC solution is shown over any finite agent solution. More generally, we proceed to extend MFC to strongly interacting agents, similar to the MFG scenario. Our final extension considers partial observability, where decentralized agents act only upon available information. Here, a framework optimizing over Lipschitz classes of policies is introduced. We obtain policy gradient approximation guarantees for the latter two settings. The frameworks are verified theoretically by showing approximate optimality of MFC, and experimentally by demonstrating performance comparable or superior to state-of-the-art multi-agent reinforcement learning algorithms. Finally, we briefly explore some potential applications of MFGs and MFC in scenarios with large populations of agents. We survey applications in distributed computing, cyber-physical systems, autonomous mobility and routing, as well as natural and social sciences. We also take a closer look at two particular applications in UAV swarm control and edge computing. In the former, we consider the effect of collision avoidance as an additional constraint for MFC in embodied robot swarms. In the latter, we compare MFG and MFC results for a computational offloading scenario. Overall, in this thesis we investigate the suitability of methods based on MFC and MFC for large-scale tractable multi-agent reinforcement learning. We contribute novel learning methods and theoretical approximation frameworks, as well as study some applications. On the whole, we find that MFGs and MFC can successfully be applied to analyze large-scale control and games, with high generality and outperforming some state-of-the-art solutions.

October 2024 32nd European Signal Processing Conference (EUSIPCO 2024) Conference

Spatial Inference Network: Indoor Proximity Detection via Multiple Hypothesis Testing

Martin Gölz, Luca O. Baudenbacher, Abdelhak M. Zoubir, Visa Koivunen

PDF BibTeX

Abstract

Spatial inference is an important task in large-scale wireless sensor networks, the Internet of Things, radio spectrum monitoring, and smart cities. In this paper, we extend and adopt our spatial multiple hypothesis testing approach with false discovery rate control to a real-world spatial inference sensor system detecting the presence of people in indoor settings. The developed inference method is data driven, using empirical statistics and conformal p-values instead of assuming specific probability models. The approach has both, low computational complexity and energy efficient communication, hence expanding the lifespan of the network. Each sensor computes local p-values and communicates them to a fusion center. This performs the actual testing and identifies the regions where the alternative hypotheses are in place. The reliable performance of the method is demonstrated using real-world measured data acquired by an indoor wireless sensor network.

October 2024 IEEE Transactions on Wireless Communications Article

Massive MIMO Multicasting with Finite Block-length

Xuzhong Zhang, Lin Xiang, Jiaheng Wang, Xiqi Gao

BibTeX DOI: 10.1109/TWC.2024.3423310

Abstract

Massive multiple-input multiple-output (MIMO) multicasting is a promising approach for simultaneously delivering common messages to multiple users in next-generation wireless networks. However, existing studies have exclusively focused on multicast beamforming designs based on the Shannon capacity, assuming the infinite blocklength (IBL) for transmission. This assumption may lead to strictly suboptimal designs for practical multicast transmissions with finite blocklength (FBL), especially in ultra-reliable low-latency communications. In this paper, we explore the beamforming design for massive MIMO multi-group multicasting in the FBL regime. Our study considers both the max-min fairness and the weighted sum rate criteria for a comprehensive treatment. Due to the non-concave FBL rate function, the resulting optimization problems are known to be notoriously hard. We characterize the necessary and sufficient condition for the non-negative FBL rate to be a concave function of the received signal-to-interference-plus-noise ratio (SINR). Considering a finite number of transmit antennas, we propose low-complexity majorization-minimization (MM) type algorithms, which update variables in either closed or semi-closed form, to achieve locally optimal solutions of the formulated optimization problems. We further show that, as the number of transmit antennas becomes large, the optimal beamformer of each group aligns asymptotically with a linear combination of the channel vectors of that group of users, where the optimal normalized combining coefficients are derived in closed form. Subsequently, we obtain the globally optimal multicast beamformers by optimizing the power allocation using low-complexity iterative algorithms. Simulation results show that the proposed schemes outperform several existing methods, especially those employing the Shannon capacity as the performance metric. Moreover, the proposed algorithms exhibit complexities that only slightly grow with the number of transmit antennas and they can notably reduce the computation time by up to two orders of magnitude over the benchmarks, making them highly beneficial for massive MIMO applications.

September 2024 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2024) Conference

AR-CP: Uncertainty-Aware Perception in Adverse Conditions with Conformal Prediction and Augmented Reality For Assisted Driving

Achref Doula, Max Mühlhäuser, Alejandro Sanchez Guinea

BibTeX DOI: 10.1109/CVPRW63382.2024.00026

Abstract

Deep learning models are pivotal in enhancing driver assistance systems and improving environmental perception. However, the tendency of neural networks towards overconfident predictions poses a risk of inaccurate predictions, potentially compromising driver safety in adverse conditions. To mitigate this issue, we introduce AR-CP, an uncertainty-aware framework designed to augment driver perception in scenarios characterized by adverse weather and insufficient lighting, through the integration of conformal prediction and augmented reality (AR). Our framework initiates with a conformal prediction step that produces an uncertainty-aware prediction set including potential object classes at a predefined probability level. Subsequently, AR is used to provide a simplified and informative visualization of the closest common parent class of the classes in the prediction set, thereby reducing the likelihood of misinformation. We provide a principled formulation and theoretical analysis of our framework. We evaluate AR-CP on the ROAD dataset, a large dataset containing different difficult situations that induce high uncertainty during prediction time. The results show that our framework outperforms state-of-the-art approaches in providing smaller prediction sets while holding the theoretical guarantees, ensuring an uncertainty-aware prediction, and reducing user confusion. We conduct an immersive user study with 15 participants to investigate the effects of our concept on the quality of perception, situation awareness, and mental load of participants. The results show that our concept facilitates a safer driving experience while holding the mental load low and the situation awareness high.

September 2024 IEEE Transactions on Wireless Communications Article

User Tracking and Direction Estimation Codebook Design for IRS-Assisted mmWave Communication

Moritz Garkisch, Sebastian Lotter, Gui Zhou, Vahid Jamali, Robert Schober

BibTeX DOI: 10.1109/TWC.2024.3463211

Abstract

Future communication systems are envisioned to employ intelligent reflecting surfaces (IRSs) and the millimeter wave (mmWave) frequency band to provide reliable high-rate services. For mobile users, the time-varying channel state information (CSI) requires adequate adjustment of the reflection pattern of the IRS. We propose a novel codebook-based user tracking (UT) algorithm for IRS-assisted mmWave communication, allowing suitable reconfiguration of the IRS unit cell phase shifts, resulting in a high reflection gain. The presented algorithm acquires the direction information of the user based on a peak maximum likelihood (ML)-based direction estimation. Using the direction information, the user’s trajectory is extrapolated to proactively update the adopted codeword and adjust the IRS phase shift configuration accordingly. Furthermore, we conduct a theoretical analysis of the direction estimation error and utilize the obtained insights to design a codebook specifically optimized for direction estimation. Our results show that the proposed ML-based direction estimation algorithm outperforms a multiple signal classification (MUSIC)-based reference scheme. The proposed direction estimation codebook improves the direction estimation error for both these schemes as compared to when a reference codebook is used. Also, the accuracy of the proposed UT algorithm is shown to be competitive with Kalman filter-based UT, while the proposed scheme requires fewer a priori assumptions on the user movement. Furthermore, the average achieved signal-to-noise ratio (SNR) as well as the average effective rate of the proposed UT algorithm are analyzed. The proposed UT algorithm requires only a low overhead for direction and channel estimation and avoids outdated IRS phase shifts. Furthermore, it is shown to outperform three benchmark schemes based on direct phase shift optimization, optimal codeword selection, and hierarchical codebook search, respectively, via computer simulations.

September 2024 35th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC’24) Conference

Outage Probability Analysis of Multi-Connectivityin UAV-Assisted Urban mmWave Communication

Zhengxin Cao, Jing Zhang, Lin Xiang, Xiaohu Ge

BibTeX

Abstract

Unmanned aerial vehicle (UAV)-assisted millimeterwave (mmWave) communication presents a promising solution for high data-rate wireless applications in urban environments. However, due to limited energy supply and communication capability, UAVs can only provide temporary communication services. This challenge motivates the exploration of three-dimensional (3D) integrated aerial and ground mmWave communications utilizing the multi-connectivity (MC) technique. By leveraging both ground and aerial mmWave links over licensed and unlicensed mmWave spectrums, respectively, the MC technique can effectively exploit spatial and frequency diversity to enhance the connectivity and reliability of UAV-assisted mmWave communications. We develop a unified framework based on stochastic geometry and Markov chain to analyze the coverage and outage probabilities of the 3D integrated aerial/ground mmWave networks. Furthermore, we show that an optimal UAV flight altitude for maximizing the coverage probability of UAV communication exists and derive it in a closed-form expression. Simulation results demonstrate that UAVs can maintain reliable mmWave connections even when connections from terrestrial mmWave base stations (BSs) are obstructed by buildings, underscoring the benefits of MC in enhancing the robustness of 3D integrated aerial and ground mmWave networks.

September 2024 35th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC’24) Conference

Completion Time Minimization for Adaptive Semi-Asynchronous Federated Learning over Wireless Networks,

S. Gan, K. Chen, J. Zhang, Lin Xiang, W. K. Ng, Xiaohu Ge

BibTeX

September 2024 5th International Conference on Resilient Systems (ICRS 2024) Conference

Enhancing Short-Term Discharge Predictions: An innovative ARIMA-iGARCH Model for Improved Flood Forecasting and Disaster Resilience

Mahshid Khazaeiathar, Britta Schmalz

BibTeX DOI: 10.3929/ethz-b-000696625

Abstract

Choosing the most suitable model for discharge simulation is challenging, especially with short-term data. While artificial neural networks excel at detecting river flow patterns, they require substantial data for training, making them less effective with limited datasets. As an alternative, Autoregressive Integrated Moving Average (ARIMA) models can be utilized for short-term data. However, severe volatilities and inherent non-stationarity in hydrological time series can introduce significant errors. This study introduces a new adaptive hybrid model, ARIMA-iGARCH (Integrated Generalized AutoRegressive Conditional Heteroscedasticity), designed to account for volatility and non-stationarity, thus minimizing errors in short-term time series modeling. The ARIMA-iGARCH model specifically addresses the inconsistency of variance and non-stationary behavior in discharge time series. We applied the ARIMA-iGARCH model to four hourly discharge time series of the Schwarzbach River upstream of the gauge Nauheim in Hesse, Germany. In this process, the iGARCH model was used for prediction, and hybrid model parameters were obtained by combining ARIMA and GARCH models, assuming a normal distribution for residuals. The results demonstrate that the new adaptive hybrid model, based on this special parameter estimation method, offers less complexity, greater accuracy, and more reliable predictions. By capturing fluctuations in time series variance, the ARIMA-iGARCH model significantly improves the modeling of long-memory, non-linear, non-stationary, and particularly short-term datasets. This improvement is crucial for disaster resilience, as accurate discharge predictions enhance flood forecasting and management. Effective flood forecasting leads to better preparedness and response strategies, mitigating the impacts of hydrological disasters. In conclusion, the ARIMA-iGARCH model represents a significant advancement for hydrological time series modeling, particularly with short-term data, contributing to disaster resilience by enabling more accurate and reliable flood predictions.

August 2024 59th IEEE Internartional Conference on Communications (ICC’24) Conference

Minimizing the Age of Incorrect Information for Status Update Systems with Energy Harvesting

Sumedh Jitendra Dongare, Aleksandar Jovovich, Wanja De Sombre, Andrea Patricia Ortiz Jimenez, Anja Klein

BibTeX DOI: 10.1109/ICC51166.2024.10622719

Abstract

Status Update Systems (SUSs) are central components in applications like environmental sensing or smart cities. They consist of a sender monitoring a remote process and sending the sensed information to a receiver. The sender aims to deliver fresh information about the monitored process’s state to allow the receiver to timely respond to the process’s changes. In SUSs, the sender is usually battery operated. Therefore, to increase the available energy we consider Energy Harvesting (EH). Moreover, as at the receiver the information transmitted by the sender is only relevant when the process’s state changes, we measure the information’s freshness using Age of Incorrect Information (AoII). Finding the optimal transmission strategy at the sender that minimizes the AoII requires perfect system knowledge, i.e., the behavior of the monitored process, the channel quality, and the available energy. However, in real applications this knowledge is usually not available. To overcome this challenge, we first establish the optimality of threshold-based policies for AoII minimization in SUSs with EH capabilities by proving that there exists an AoII value depending on the observed state of the monitored process, the battery level and the receiver’s estimation of the monitored process’s state beyond which transmitting is preferable over idling. Next, we exploit the threshold-based policies’ structure and deploy a learning algorithm based on Finite-Difference Policy Gradient (FDPG). Our proposed approach finds the AoII thresholds without requiring perfect system knowledge. Simulations show that our approach outperforms reference algorithms by at least 20% and efficiently learns near-optimal policies for AoII minimization.

August 2024 59th IEEE Internartional Conference on Communications (ICC’24) Conference

Age of Information Minimization in Status Update Systems with Imperfect Feedback Channel

Friedrich Pyttel, Wanja De Sombre, Andrea Patricia Ortiz Jimenez, Anja Klein

BibTeX DOI: 10.1109/ICC51166.2024.10622227

Abstract

Status Update System (SUS) are monitoring applications of Internet of Things (IoT). They are formed by a sender that monitors a remote process and sends status updates to a receiver over a wireless channel. For successful monitoring, the sender must keep the status updates at the receiver fresh. This freshness is generally measured using the Age of Information (AoI) metric. The aim of the sender is to find a monitoring and transmission strategy that minimizes the AoI. To find the optimal strategy, the sender needs to accurately track the AoI at the receiver, i.e., it needs to perfectly know whether a transmitted status update is correctly received or not. This knowledge can be achieved by using a feedback channel between receiver and sender to send acknowledge (ACK) or negative acknowledge (NACK) messages. However, in real applications, the feedback channel is not perfect, and the transmission of ACK/NACK messages might fail. This means, the monitoring and transmission decisions have to be made under uncertainty about the receiver’s AoI. To overcome this challenge, we introduce the concept of a socalled belief distribution and propose a joint monitoring and transmission strategy at the sender based on reinforcement learning. Our approach, termed Belief Learning, exploits the belief distribution to minimize the AoI at the receiver. Through numerical simulations we show that Belief Learning enables the sender to achieve near-optimal performance with respect to the perfect feedback channel case.

August 2024 59th IEEE Internartional Conference on Communications (ICC’24) Conference

Two-Sided Learning: A Techno-Economic View of Mobile Crowdsensing under Incomplete Information

Sumedh Jitendra Dongare, Bernd Simon, Andrea Patricia Ortiz Jimenez, Anja Klein

BibTeX

Abstract

In Mobile Crowdsensing (MCS) a mobile crowd-sensing platform (MCSP) collects sensing data from mobile units (MUs) in exchange for payment. The MCSP broadcasts a list of available sensing tasks. Based on this list, each MU solves a task proposal problem to decide which task it is willing to perform and sends a proposal to the MCSP. Based on the MUs’ proposals, the MCSP solves a task assignment problem. There are two challenges when finding efficient task proposal strategies for the MUs and an efficient task assignment strategy for the MCSP (i) The techno-economic perspective of MCS: From the technical perspective, MCS should maximize the data quality while minimizing time and energy consumption. From the economic perspective, there are two sides, the MUs and the MCSP which act as selfish decision-makers, who aim at maximizing their own income. (ii) Incomplete information at two sides: Initially, the MCSP does not know the expected data quality and the MUs do not know the expected effort required for task completion. To overcome these challenges, we propose a novel Two-Sided Learning (TSL) approach. At the MU side, TSL is based on an innovative gradient-based multi-armed bandit solution to maximize the MUs’ utility under incomplete information about the strategies of other MUs. At the MCSP side, a learning strategy is used to find the task assignment strategy that maximizes its utility. Simulation results show that TSL achieves near-optimal social welfare, which is the sum of MUs’ and MCSP’s utilities, and a near-optimal energy efficiency.

August 2024 59th IEEE Internartional Conference on Communications (ICC’24) Conference

Joint Optimization of Beamforming and 3D Array-Steering for UAV-Aided ISAC

Fengcheng Pei, Lin Xiang, Anja Klein

BibTeX DOI: 10.1109/ICC51166.2024.10622806

Abstract

In this paper, we investigate unmanned aerial vehicle (UAV)-aided integrated sensing and communication (ISAC) by employing a uniform linear array (ULA) of patch antennas onboard the UAV. The three-dimensional (3D) directional gain pattern of the patch antennas and the array beamforming are jointly exploited to facilitate efficient ISAC signal transmission for sensing multiple targets and communicating with multiple users. Assuming the positions of the targets and users are known, we jointly optimize the beamforming and 3D array-steering of the patch antenna array to maximize the sum of transmit beam-pattern gains towards the targets while guaranteeing quality-of-service (QoS) for each communication user. The formulated optimization problem is nonconvex and generally intractable. Exploiting the special structures underlying the problem, we propose a low-complexity iterative algorithm based on proximal block coordinate descent (BCD) to decompose the problem into several convex and manifold optimization subproblems and iteratively solve them. Simulation results verify the benefits of joint beamforming and 3D steering optimization for UAV-aided ISAC using patch antenna array, particularly when the communication QoS requirements are stringent.

August 2024 59th IEEE Internartional Conference on Communications (ICC’24) Conference

Joint Communication and Computing Optimization for Digital Twin Synchronization with Aerial Relay

Yi Wang, Markus Krantzik, Lin Xiang, Anja Klein

BibTeX DOI: 10.1109/ICC51166.2024.10622630

Abstract

Digital twin (DT) applications usually need to be synchronized in real time with the state of the physical system (PS). This process includes both synchronizing the data collected by sensors in the PS to a server and updating the DT at the server in adaptation to the dynamics of the PS. However, communicating with power- and rate-limited sensors and processing high-volume sensed data with limited computing resources present significant challenges for DT synchronization. In this paper, we tackle both bottlenecks by proposing a joint communication and computing design for DT synchronization. In particular, we exploit buffering at an aerial cluster head of the sensors and enable buffer-aided (BA) relaying to increase the communication throughput of the sensors during data synchronization. Moreover, we adopt a novel stream computing scheme, which allows for DT updating in parallel with data synchronization, to accelerate DT synchronization. To maximize the performance of the proposed approach, we jointly optimize the trajectory of the aerial relay and the communication and computing resource allocation for minimizing the DT synchronization time. The formulated problem is a mixed-integer nonconvex problem, which is generally intractable. To tackle this challenge, we propose a low-complexity two-layer iterative suboptimal algorithm. Our simulation results show that the DT synchronization time can be significantly reduced by up to 43.8%, through stream computing and the joint optimization of the relay’s trajectory and the communication/computing resource allocation.

August 2024 The 12th IFAC Symposium on Control of Power & Energy Systems Conference

Robust and Chance-Constrained Dispatch Policies for Linear Power Systems

Hans Stenglein, Timm Faulwasser, Florian Steinke

PDF BibTeX DOI: 10.1016/j.ifacol.2024.07.463

Abstract

Several methods have recently been proposed to determine optimal feedback control policies for generator dispatch under uncertain grid in-feeds. This work presents and explains two such approaches based on a constrained linear power flow model in a unified notation, a robust method based on linear programming and a chance-constrained one based on polynomial chaos expansions and second order cone programming. This work compares both methods on the same small test example and discusses consequences for long-term grid expansion planning. While the robust method is easier to implement, it is also not more restrictive. For the chance-constrained method, we highlight a serious drawback in reformulations common in the literature. As an aside, we extend the existing formulation to cases where not all disturbances can be measured, and combine it with a robust pre-processing scheme.

August 2024 59th IEEE Internartional Conference on Communications (ICC’24) Conference

Risk-Aware Bandits for Digital Twin Placement in Non-Stationary Mobile Edge Computing

Maximilian Wirth, Andrea Patricia Ortiz Jimenez, Anja Klein

BibTeX DOI: 10.1109/ICCWorkshops59551.2024.10615914

August 2024 59th IEEE Internartional Conference on Communications (ICC’24) Conference

Joint Beamforming and Trajectory Optimization for UAV-Enabled ISAC under a Finite Energy Budget

Burak Yilmaz, Lin Xiang, Anja Klein

BibTeX DOI: 10.1109/ICCWorkshops59551.2024.10615353

Abstract

This paper studies joint beamforming and trajectory optimization for integrated sensing and communication (ISAC) enabled by an energy-constrained unmanned aerial vehicle (DAV). The DAV transmits information-bearing signals using an onboard uniform linear array (ULA), for simultaneously serving downlink communication users and sensing multiple targets during its flight. To explore the synergy between UAV and ISAC, we jointly optimize the DAV’s flight trajectory, ISAC beamforming, and mission completion time for maximizing the accumulated sensing energy for ground targets under a finite energy budget for the UAV, while guaranteeing quality-of-service for communication users. The formulated problem is highly non-convex, which is generally intractable. Motivated by the success of approximate dynamic programming (DP) methods, we propose a novel low-complexity high-quality solution by combining the one-step lookahead rollout algorithm in approximate DP and the semidefinite programming technique in convex optimization. Our simulation results show that, compared to two baseline schemes, the proposed scheme can significantly enlarge the achievable sensing and communication performance region for ISAC.

July 2024 19th International Conference on Availability, Reliability and Security (ARES2024) Conference

Navigating the landscape of IoT security and associated risks in critical infrastructures

Andrej Pastorek, Andrea Tundis

BibTeX DOI: 10.1145/3664476.3669979

Abstract

The Internet of Things (IoT) presents transformative opportunities for connectivity and automation across various sectors, but it also introduces significant security risks that need to be comprehensively addressed. Indeed, the growing integration of IoT devices, including their vulnerabilities, into critical infrastructures amplifies potential risks in daily life, making these systems prime targets for cybercriminal activities, including espionage and sabotage. Cases where IoT devices have been misused, due to firmware vulnerabilities, embedded passwords, and hidden backdoors are real-world scenarios, that pose significant threats to privacy and security. That’s why this paper aims to point out the urgency of addressing these issues as IoT applications continue to proliferate across healthcare, transportation, urban development and other sectors. Different types of vulnerabilities and their implications with focus on urban critical infrastructures, which can lead to severe consequences like energy blackouts, water contamination, and widespread service disruptions, especially in densely populated areas, are discussed. Moreover, the need of a multidimensional approach that encompasses technological, legal, social, and economic considerations, to deal with those broader cybersecurity and risk management implications of IoT is highlighted. As a consequence, the need for continuous evolution in security strategies to keep pace with the rapid advancements in IoT technologies is pointed out, thus arguing for a proactive approach to safeguard IoT systems against emerging threats and to ensure the safe and resilient operation of these increasingly integral parts of modern critical infrastructures.

July 2024 25th IEEE Wireless Communications and Networking Conference (WCNC2024) Conference

Joint Optimization of Beamforming and 3D Array Steering for Multi-antenna UAV Communications

Lin Xiang, Fengcheng Pei, Anja Klein

BibTeX DOI: 10.1109/WCNC57260.2024.10570943

Abstract

In this paper, we consider unmanned aerial vehicle (UAV)-aided downlink communication using a rotatable array of directional antennas such as half-wavelength dipoles. The antenna array is mounted onboard the UAV using a gimbal and can be flexibly rotated in the three-dimensional (3D) space. As such, the directional gain pattern of dipoles and the array beamforming can be both best exploited to facilitate efficient information transmission to multiple low-priority (or secondary) users while mitigating co-channel interference for another high-priority (or primary) user coexisting with but not served by the UAV. Assuming that the direction of the high-priority user is known, we jointly optimize the electrical beamforming and mechanical steering of the rotatable dipole array for maximizing the weighted sum-rate achievable by the low-priority users while minimizing the interference power radiated in the direction of the high-priority user. The formulated optimization problem is nonconvex and generally intractable. Exploiting its special problem structure, we decompose the problem into several convex and manifold optimization subproblems and further propose a low-complexity iterative algorithm based on proximal block coordinate descent for solution. Our simulation results verify the convergence of the proposed algorithm. Moreover, compared with systems employing non-rotatable and rotatable arrays of isotropic antennas, the rotatable dipole array can flexibly adjust the gain patterns in different azimuth and elevation angles to increase the communication throughput by up to 300% and 77%, respectively.

June 2024 IEEE 2024 International Conference on Communications Conference

Fast Transition-Aware Reconfiguration of Liquid Crystal-based RISs

Mohamadreza Delbari, Robin Neuder, Alejandro Jiménez-Sáez, Arash Asadi, Vahid Jamali

BibTeX

June 2024 22nd Annual International Conference on Mobile Systems, Applications and Services Conference

Leveraging Apple’s Find My Network for Large-Scale Distributed Sensing

Max Granzow, Alexander Heinrich, Matthias Hollick, Marco Zimmerling

PDF BibTeX DOI: 10.1145/3643832.3661412

Abstract

Find My is a crowd-sourced network of hundreds of millions of Apple devices that use Bluetooth Low Energy (BLE) to detect and track the location of items. We explore the limits and opportunities of using this proprietary network for large-scale distributed sensing. The key idea is to let low-cost sensing devices emit specially crafted BLE advertisements that trick nearby Apple devices into generating location reports that carry arbitrary sensor data, which can then be retrieved from the Apple servers. This paper reports on our ongoing work to reverse engineer the Find My system and to design a protocol for the efficient and reliable collection of data from sensing devices via the Find My network. Preliminary results from real-world experiments demonstrate the feasibility of our approach and a several-fold performance improvement compared with the state of the art.

June 2024 22nd Annual International Conference on Mobile Systems, Applications and Services (MOBISYS ‘24) Conference

Always On Air: Adaptive Physical Layer Switching For Uninterrupted UAV Air-to-Ground Communication

Vincenz Mechler, Felix Wiegand, Matthias Hollick, Bastian Bloessl

BibTeX DOI: 10.1145/3661810.3663467

Abstract

Reliable wireless communication is crucial for remote operation of Unmanned Aerial Vehicles (UAVs). Yet, staying in control of the vehicle at all times poses a great challenge, given the dynamics of the wireless channel. Existing technologies are optimized for a given application and, therefore, not well suited for this use case, as they cannot provide both high throughput in high Signal to Noise Ratio (SNR) regimes and high reliability in low SNR regimes. To overcome this limitation, we propose a channel-aware predictive physical layer switching algorithm, utilizing the UAV’s telemetry data for implicit synchronization. We evaluate our system experimentally in a fully emulated testbed, achieving an overall outage probability as low as 0.7 % while increasing the average throughput.

June 2024 IEEE Transactions on Biomedical Engineering Article

Emergency Response Person Localization and Vital Sign Estimation Using a Semi-Autonomous Robot Mounted SFCW Radar

Christian A. Schroth, Christian Eckrich, Ibrahim Kakouche, Stefan Fabian, Oskar von Stryk, Abdelhak M. Zoubir, Michael Muma

BibTeX DOI: 10.1109/TBME.2024.3350789

Abstract

The large number and scale of natural and man-made disasters have led to an urgent demand for technologies that enhance the safety and efficiency of search and rescue teams. Semi-autonomous rescue robots are beneficial, especially when searching inaccessible terrains, or dangerous environments, such as collapsed infrastructures. For search and rescue missions in degraded visual conditions or non-line of sight scenarios, radar-based approaches may contribute to acquire valuable, and otherwise unavailable information. This article presents a complete signal processing chain for radar-based multi-person detection, 2D-MUSIC localization and breathing frequency estimation. The proposed method shows promising results on a challenging emergency response dataset that we collected using a semi-autonomous robot equipped with a commercially available through-wall radar system. The dataset is composed of 62 scenarios of various difficulty levels with up to five persons captured in different postures, angles and ranges including wooden and stone obstacles that block the radar line of sight. Ground truth data for reference locations, respiration, electrocardiogram, and acceleration signals are included.

June 2024 Other

Resilienz in der digitalisierten Landwirtschaft: Abhängigkeiten deutscher landwirtschaftlicher Betriebe von Kommunikations- und Energieinfrastruktur im Katastrophenschutz

Franz Kuntke, Christian Reuter

PDF BibTeX DOI: 10.5281/zenodo.12209182

Abstract

Die Landwirtschaft erfährt eine kontinuierliche Digitalisierung, wobei die Bedeutung von Daten für die eingesetzten Werkzeuge zunimmt. Im Gegensatz zu anderen kritischen Infrastrukturen hat der durchschnittliche landwirtschaftliche Betrieb eine geringe Anzahl von Mitarbeiter:innen. Die Anforderungen an die Landtechnik, ihre Umsetzung und die Vorschriften unterscheiden sich deshalb von anderer KRITIS. Unklar bleiben dabei die Auswirkungen aktueller Trends wie Smart Farming auf die Widerstandsfähigkeit des Sektors und Abhängigkeiten von anderen Infrastrukturen. Einige Aspekte der landwirtschaftlichen Digitalisierung müssen dabei kritisch betrachtet werden, um hohe Sicherheitsrisiken in Zukunft zu vermeiden: Produkte müssen sichere Voreinstellungen haben und auch die Notwendigkeit von Cloud-Anbindung sollte häufiger hinterfragt werden – sowohl für eine stärkere Sicherheit als auch Resilienz gegenüber Infrastrukturausfällen und dem hohen Datenschutzbedürfnis in der Landwirtschaft. Mit richtigen Entwicklungen kann dabei die Digitalisierung nicht nur sicher gestaltet werden, sondern auch positiv auf die Resilienz und Effizienz der Betriebe wirken. Schlagworte: Smart Farming, Landwirtschaft, Kritische Infrastruktur, Resilienz, Dependenz

May 2024 Other

Kooperative Resilienz: Ein Leitfaden für Städte und Kommunen zur Bürgerbeteiligung im Katastrophenschutz

Michèle Knodt, Alice Engel, Sonja Petersen, Marie Kluge, Christine Quittkat, Hans-Joachim Linke

PDF BibTeX DOI: 10.5281/zenodo.11401376

May 2024 27th International Workshop on Smart Antennas (WSA2024) Conference

Joint Beamforming and Trajectory Optimization for UAV-Aided ISAC with Dipole Antenna Array

Mustafa Burak Yilmaz, Lin Xiang, Anja Klein

BibTeX

May 2024 12th International Conference on Learning Representations (ICLR 2024) Conference

Learning Mean Field Games on Sparse Graphs: A Hybrid Graphex Approach

Christian Fabian, Kai Cui, Heinz Koeppl

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Abstract

Learning the behavior of large agent populations is an important task for numerous research areas. Although the field of multi-agent reinforcement learning (MARL) has made significant progress towards solving these systems, solutions for many agents often remain computationally infeasible and lack theoretical guarantees. Mean Field Games (MFGs) address both of these issues and can be extended to Graphon MFGs (GMFGs) to include network structures between agents. Despite their merits, the real world applicability of GMFGs is limited by the fact that graphons only capture dense graphs. Since most empirically observed networks show some degree of sparsity, such as power law graphs, the GMFG framework is insufficient for capturing these network topologies. Thus, we introduce the novel concept of Graphex MFGs (GXMFGs) which builds on the graph theoretical concept of graphexes. Graphexes are the limiting objects to sparse graph sequences that also have other desirable features such as the small world property. Learning equilibria in these games is challenging due to the rich and sparse structure of the underlying graphs. To tackle these challenges, we design a new learning algorithm tailored to the GXMFG setup. This hybrid graphex learning approach leverages that the system mainly consists of a highly connected core and a sparse periphery. After defining the system and providing a theoretical analysis, we state our learning approach and demonstrate its learning capabilities on both synthetic graphs and real-world networks. This comparison shows that our GXMFG learning algorithm successfully extends MFGs to a highly relevant class of hard, realistic learning problems that are not accurately addressed by current MARL and MFG methods.

May 2024 12th International Conference on Learning Representations (ICLR 2024) Conference

Learning Decentralized Partially Observable Mean Field Control for Artificial Collective Behavior

Kai Cui, Sascha Hauck, Christian Fabian, Heinz Koeppl

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Abstract

Recent reinforcement learning (RL) methods have achieved success in various domains. However, multi-agent RL (MARL) remains a challenge in terms of decentralization, partial observability and scalability to many agents. Meanwhile, collective behavior requires resolution of the aforementioned challenges, and remains of importance to many state-of-the-art applications such as active matter physics, self-organizing systems, opinion dynamics, and biological or robotic swarms. Here, MARL via mean field control (MFC) offers a potential solution to scalability, but fails to consider decentralized and partially observable systems. In this paper, we enable decentralized behavior of agents under partial information by proposing novel models for decentralized partially observable MFC (Dec-POMFC), a broad class of problems with permutation-invariant agents allowing for reduction to tractable single-agent Markov decision processes (MDP) with single-agent RL solution. We provide rigorous theoretical results, including a dynamic programming principle, together with optimality guarantees for Dec-POMFC solutions applied to finite swarms of interest. Algorithmically, we propose Dec-POMFC-based policy gradient methods for MARL via centralized training and decentralized execution, together with policy gradient approximation guarantees. In addition, we improve upon state-of-the-art histogram-based MFC by kernel methods, which is of separate interest also for fully observable MFC. We evaluate numerically on representative collective behavior tasks such as adapted Kuramoto and Vicsek swarming models, being on par with state-of-the-art MARL. Overall, our framework takes a step towards RL-based engineering of artificial collective behavior via MFC.

March 2024 Proceedings of the AAAI Conference on Artificial Intelligence Conference

Learning Discrete-Time Major-Minor Mean Field Games

Kai Cui, Gökce Dayanikli, Mathieu Laurière, Matthieu Geist, Oliver Pietquin, Heinz Koeppl

BibTeX DOI: 10.1609/aaai.v38i9.28818

Abstract

Recent techniques based on Mean Field Games (MFGs) allow the scalable analysis of multi-player games with many similar, rational agents. However, standard MFGs remain limited to homogeneous players that weakly influence each other, and cannot model major players that strongly influence other players, severely limiting the class of problems that can be handled. We propose a novel discrete time version of major-minor MFGs (M3FGs), along with a learning algorithm based on fictitious play and partitioning the probability simplex. Importantly, M3FGs generalize MFGs with common noise and can handle not only random exogeneous environment states but also major players. A key challenge is that the mean field is stochastic and not deterministic as in standard MFGs. Our theoretical investigation verifies both the M3FG model and its algorithmic solution, showing firstly the well-posedness of the M3FG model starting from a finite game of interest, and secondly convergence and approximation guarantees of the fictitious play algorithm. Then, we empirically verify the obtained theoretical results, ablating some of the theoretical assumptions made, and show successful equilibrium learning in three example problems. Overall, we establish a learning framework for a novel and broad class of tractable games.

March 2024 12th International Conference on Learning Representations (ICLR 2024) Conference

Common Sense Initialization of Mixture Density Networks for Motion Planning with Overestimated Number of Components.

Thomas Kreutz, Max Mühlhäuser, Alejandro Sanchez Guinea

PDF BibTeX

Abstract

Mixture density networks (MDNs) are a natural choice to model multi-modal predictions for trajectory prediction or motion planning. However, MDNs are often difficult to train due to mode collapse and a need for careful initialization, which becomes even more problematic when the number of mixture components are strongly overestimated. To address this issue in motion planning problems, we propose a pre-training scheme for MDNs called common sense initialization (CSI). Pre-training with CSI allows variety-encouraging optimization such as Winner-Takes-All (WTA) to exploit the initialized weights during training, so that the MDN can converge when the number of components are overestimated. This paper presents empirical evidence for the effectiveness of CSI when applied to motion planning of pedestrian agents in urban environments.

March 2024 Journal of Business Venturing Article

Flip the tweet - the two-sided coin of entrepreneurial empathy and its ambiguous influence on new product development

Konstantin Kurz, Carolin Bock, Leonard Hanschur

PDF BibTeX DOI: 10.1016/j.jbusvent.2023.106378

Abstract

Is empathy a uniformly good thing for entrepreneurs? Contrasting the hitherto predominantly positive view advocated by the extant entrepreneurship literature, we develop a novel model of entrepreneurial empathy’s mechanisms and suggest a too-much-of-a-good-thing perspective. We empirically confirm this model using a dataset of 4425 real entrepreneurs, where we find that empathy influences entrepreneurial new product development as an essential entrepreneurial activity in an inverted U-shaped pattern. We further show that empathy’s negative effects are particularly detrimental for very anxious entrepreneurs. These findings provide strong evidence for considering entrepreneurial empathy an important but highly ambiguous success factor.

February 2024 IEEE Transactions on Communications Article

Optical IRSs: Power Scaling Law, Optimal Deployment, and Comparison With Relays

Hedieh Ajam, Marzieh Najafi, Vahid Jamali, Robert Schober

BibTeX DOI: 10.1109/TCOMM.2023.3327464

Abstract

The line-of-sight (LOS) requirement of free-space optical (FSO) systems can be relaxed by employing optical relays or optical intelligent reflecting surfaces (IRSs). In this paper, we show that the power reflected from FSO IRSs and collected at the receiver (Rx) lens may scale quadratically or linearly with the IRS size or may saturate at a constant value. We analyze the power scaling law for optical IRSs and unveil its dependence on the wavelength, transmitter (Tx)-to-IRS and IRS-to-Rx distances, beam waist, and Rx lens size. We also consider the impact of linear, quadratic, and focusing phase shift profiles across the IRS on the power collected at the Rx lens for different IRS sizes. Our results reveal that surprisingly the powers received for the different phase shift profiles are identical, unless the IRS operates in the saturation regime. Moreover, IRSs employing the focusing (linear) phase shift profile require the largest (smallest) size to reach the saturation regime. We also compare optical IRSs in different power scaling regimes with optical relays in terms of the outage probability, diversity and coding gains, and optimal placement. Our results show that, at the expense of a higher hardware complexity, relay-assisted FSO links yield a better outage performance at high signal-to-noise-ratios (SNRs), but optical IRSs can achieve a higher performance at low SNRs. Moreover, while it is optimal to place relays equidistant from Tx and Rx, the optimal location of optical IRSs depends on the phase shift profile and the power scaling regime they operate in.

January 2024 Other

FDR-Controlled Portfolio Optimization for Sparse Financial Index Tracking

Jasin Machkour, Daniel P. Palomar, Michael Muma

BibTeX DOI: 10.48550/arXiv.2401.15139

Abstract

In high-dimensional data analysis, such as financial index tracking or biomedical applications, it is crucial to select the few relevant variables while maintaining control over the false discovery rate (FDR). In these applications, strong dependencies often exist among the variables (e.g., stock returns), which can undermine the FDR control property of existing methods like the model-X knockoff method or the T-Rex selector. To address this issue, we have expanded the T-Rex framework to accommodate overlapping groups of highly correlated variables. This is achieved by integrating a nearest neighbors penalization mechanism into the framework, which provably controls the FDR at the user-defined target level. A real-world example of sparse index tracking demonstrates the proposed method’s ability to accurately track the S&P 500 index over the past 20 years based on a small number of stocks. An open-source implementation is provided within the R package TRexSelector on CRAN.

January 2024 Other

High-Dimensional False Discovery Rate Control for Dependent Variables

Jasin Machkour, Michael Muma, Daniel P. Palomar

BibTeX DOI: 10.48550/arXiv.2401.15796

Abstract

Algorithms that ensure reproducible findings from large-scale, high-dimensional data are pivotal in numerous signal processing applications. In recent years, multivariate false discovery rate (FDR) controlling methods have emerged, providing guarantees even in high-dimensional settings where the number of variables surpasses the number of samples. However, these methods often fail to reliably control the FDR in the presence of highly dependent variable groups, a common characteristic in fields such as genomics and finance. To tackle this critical issue, we introduce a novel framework that accounts for general dependency structures. Our proposed dependency-aware T-Rex selector integrates hierarchical graphical models within the T-Rex framework to effectively harness the dependency structure among variables. Leveraging martingale theory, we prove that our variable penalization mechanism ensures FDR control. We further generalize the FDR-controlling framework by stating and proving a clear condition necessary for designing both graphical and non-graphical models that capture dependencies. Additionally, we formulate a fully integrated optimal calibration algorithm that concurrently determines the parameters of the graphical model and the T-Rex framework, such that the FDR is controlled while maximizing the number of selected variables. Numerical experiments and a breast cancer survival analysis use-case demonstrate that the proposed method is the only one among the state-of-the-art benchmark methods that controls the FDR and reliably detects genes that have been previously identified to be related to breast cancer. An open-source implementation is available within the R package TRexSelector on CRAN.

January 2024 IEEE Open Journal of the Communications Society Article

Deterministic K-Identification for MC Poisson Channel With Inter-Symbol Interference

Mohammad Javad Salariseddigh, Vahid Jamali, Uzi Pereg, Holger Boche, Christian Deppe, Robert Schober

BibTeX DOI: 10.1109/OJCOMS.2024.3359186

Abstract

Various applications of molecular communications (MCs) feature an alarm-prompt behavior for which the prevalent Shannon capacity may not be the appropriate performance metric. The identification capacity as an alternative measure for such systems has been motivated and established in the literature. In this paper, we study deterministic K-identification (DKI) for the discrete-time Poisson channel (DTPC) with inter-symbol interference (ISI), where the transmitter is restricted to an average and a peak molecule release rate constraint. Such a channel serves as a model for diffusive MC systems featuring long channel impulse responses and employing molecule-counting receivers. We derive lower and upper bounds on the DKI capacity of the DTPC with ISI when the size of the target message set K and the number of ISI channel taps L may grow with the codeword length n . As a key finding, we establish that for deterministic encoding, assuming that K and L both grow sub-linearly in n , i.e., K=2κlogn and L=2llogn with κ+4l∈[0,1) , where κ∈[0,1) is the identification target rate and l∈[0,1/4) is the ISI rate, then the number of different messages that can be reliably identified scales super-exponentially in n , i.e., ∼2(nlogn)R , where R is the DKI coding rate. Moreover, since l and κ must fulfill κ+4l∈[0,1) , we show that optimizing l (or equivalently the symbol rate) leads to an effective identification rate [bits/s] that scales sub-linearly with n . This result is in contrast to the typical transmission rate [bits/s] which is independent of n .

January 2024 Other

False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening

Taulant Koka, Jasin Machkour, Michael Muma

BibTeX DOI: 10.48550/arXiv.2401.09979

Abstract

Gaussian graphical models emerge in a wide range of fields. They model the statistical relationships between variables as a graph, where an edge between two variables indicates conditional dependence. Unfortunately, well-established estimators, such as the graphical lasso or neighborhood selection, are known to be susceptible to a high prevalence of false edge detections. False detections may encourage inaccurate or even incorrect scientific interpretations, with major implications in applications, such as biomedicine or healthcare. In this paper, we introduce a nodewise variable selection approach to graph learning and provably control the false discovery rate of the selected edge set at a self-estimated level. A novel fusion method of the individual neighborhoods outputs an undirected graph estimate. The proposed method is parameter-free and does not require tuning by the user. Benchmarks against competing false discovery rate controlling methods in numerical experiments considering different graph topologies show a significant gain in performance.

January 2024 Other

Sparse PCA with False Discovery Rate Controlled Variable Selection

Jasin Machkour, Arnaud Breloy, Michael Muma, Daniel P. Palomar, Frédéric Pascal

BibTeX DOI: 10.48550/arXiv.2401.08375

Abstract

Sparse principal component analysis (PCA) aims at mapping large dimensional data to a linear subspace of lower dimension. By imposing loading vectors to be sparse, it performs the double duty of dimension reduction and variable selection. Sparse PCA algorithms are usually expressed as a trade-off between explained variance and sparsity of the loading vectors (i.e., number of selected variables). As a high explained variance is not necessarily synonymous with relevant information, these methods are prone to select irrelevant variables. To overcome this issue, we propose an alternative formulation of sparse PCA driven by the false discovery rate (FDR). We then leverage the Terminating-Random Experiments (T-Rex) selector to automatically determine an FDR-controlled support of the loading vectors. A major advantage of the resulting T-Rex PCA is that no sparsity parameter tuning is required. Numerical experiments and a stock market data example demonstrate a significant performance improvement.

2024 23rd International Conference on Modelling and Applied Simulation Conference

Safeguarding Critical Infrastructures with Digital Twins and AI

Andrea Tundis, Oscar Hernan Ramirez Agudelo

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Abstract

Critical infrastructures (CIs) are complex systems that are integral parts of our daily lives. We use them to access various services related to basic needs, to obtain water, energy and information, to move from one place to another, to work remotely, and so on. It is therefore essential, but challenging, to secure and protect them. On the other hand, digital twins (DTs) and artificial intelligence (AI) represent solid approaches that are well suited to modelling and analyzing complex systems, respectively. In this context, this work can be seen as a white paper that aims first to explore the main characteristics and limitations of DTs and AI when considered in isolation, and then to discuss how their combination as an intelligent entity - which represents a paradigm shift in the protection and resilience of CIs - might be beneficial to overcome such challenges and thus be useful to enhance their protection

2024 IEEE Transactions on Power Systems Article

Admissible Control Laws for Constrained Linear Power Flow: The General Case

Edwin Mora, Florian Steinke

BibTeX DOI: 10.1109/TPWRS.2023.3263365

2024 Media, War & Conflict Article

Smartphone resilience: ICT in Ukrainian civic response to the Russian full-scale invasion

Kateryna Zarembo, Michèle Knodt, Jannis Kachel

PDF BibTeX DOI: doi:10.1177/17506352241236449

Abstract

In modern warfare, digitalization has blurred the line where civilian ends and military begins. Embedded in the participative warfare theoretical paradigm, this article looks into how the information and communication technologies (ICT) enable civic resilience under the conditions of the foreign armed aggression. Specifically, the authors explore how smartphones and smartphone applications empowered the Ukrainian civil society in the aftermath of the Russian full-scale invasion of 2022. Based on an online survey and semi-structured interviews, the article highlights how the device and its features not only allowed civilians to adapt to living in conditions of a constant threat, but also to respond and support the defence from the rear. The authors conclude that, while the smartphone becomes an ‘online resilience hub’, acquiring many new functions like a mobile office, an online volunteer (frontline logistics and procurement) hub, an air-threat warner, a first-hand news source and so on, its security provision functions are not unconditional and may turn to the opposite, depending on the physical circumstances on the ground as well as the virtual information battlefield.

December 2023 IEEE Transactions on Signal Processing Article

Fast and Robust Sparsity-Aware Block Diagonal Representation

A. Taştan, Michael Muma, A. M. Zoubir

BibTeX DOI: 10.1109/TSP.2023.3343565

Abstract

The block diagonal structure of an affinity matrix is a commonly desired property in cluster analysis because it represents clusters of feature vectors by non-zero coefficients that are concentrated in blocks. However, recovering a block diagonal affinity matrix is challenging in real-world applications, in which the data may be subject to outliers and heavy-tailed noise that obscure the hidden cluster structure. To address this issue, we first analyze the effect of different fundamental outlier types in graph-based cluster analysis. A key idea that simplifies the analysis is to introduce a vector that represents a block diagonal matrix as a piece-wise linear function of the similarity coefficients that form the affinity matrix. We reformulate the problem as a robust piece-wise linear fitting problem and propose a Fast and Robust Sparsity-Aware Block Diagonal Representation (FRS-BDR) method, which jointly estimates cluster memberships and the number of blocks. Comprehensive experiments on a variety of real-world applications demonstrate the effectiveness of FRS-BDR in terms of clustering accuracy, robustness against corrupted features, computation time and cluster enumeration performance.

December 2023 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Conference

The Informed Elastic Net for Fast Grouped Variable Selection and FDR Control in Genomics Research

Jasin Machkour, Michael Muma, Daniel P. Palomar

BibTeX DOI: 10.1109/CAMSAP58249.2023.10403489

Abstract

Modern genomics research relies on genome-wide association studies (GWAS) to identify the few genetic variants among potentially millions that are associated with diseases of interest. Only reproducible discoveries of groups of associations improve our understanding of complex polygenic diseases and enable the development of new drugs and personalized medicine. Thus, fast multivariate variable selection methods that have a high true positive rate (TPR) while controlling the false discovery rate (FDR) are crucial. Recently, the T-Rex+GVS selector, a version of the T-Rex selector that uses the elastic net (EN) as a base selector to perform grouped variable election, was proposed. Although it significantly increased the TPR in simulated GWAS compared to the original T-Rex, its comparably high computational cost limits scalability. Therefore, we propose the informed elastic net (IEN), a new base selector that significantly reduces computation time while retaining the grouped variable selection property. We quantify its grouping effect and derive its formulation as a Lasso-type optimization problem, which is solved efficiently within the T-Rex framework by the terminated LARS algorithm. Numerical simulations and a GWAS study demonstrate that the proposed T-Rex+GVS (IEN) exhibits the desired grouping effect, reduces computation time, and achieves the same TPR as T-Rex+GVS (EN) but with lower FDR, which makes it a promising method for large-scale GWAS.

December 2023 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing Conference

Solving FDR-Controlled Sparse Regression Problems with Five Million Variables on a Laptop

Fabian Scheidt, Jasin Machkour, Michael Muma

BibTeX DOI: 10.1109/CAMSAP58249.2023.10403478

Abstract

Currently, there is an urgent demand for scalable multivariate and high-dimensional false discovery rate (FDR)-controlling variable selection methods to ensure the reproducibility of discoveries. However, among existing methods, only the recently proposed Terminating-Random Experiments (T-Rex) selector scales to problems with millions of variables, as encountered in, e.g., genomics research. The T-Rex selector is a new learning framework based on early terminated random experiments with computer-generated dummy variables. In this work, we propose the Big T-Rex, a new implementation of T-Rex that drastically reduces its Random Access Memory (RAM) consumption to enable solving FDR-controlled sparse regression problems with millions of variables on a laptop. We incorporate advanced memory-mapping techniques to work with matrices that reside on solid-state drive and two new dummy generation strategies based on permutations of a reference matrix. Our numerical experiments demonstrate a drastic reduction in memory demand and computation time. We showcase that the Big T-Rex can efficiently solve FDR-controlled Lasso-type problems with five million variables on a laptop in thirty minutes. Our work empowers researchers without access to high-performance clusters to make reproducible discoveries in large-scale high-dimensional data.

December 2023 2023 International Conference on Embedded Wireless Systems and Networks Conference

Demos: Robust Orchestration for Autonomous Networking

Andreas Biri, Marco Zimmerling, Lothar Thiele

PDF BibTeX

Abstract

Research in wireless sensor networks has resulted in a re- markable breadth of highly capable systems. However, while specialized protocols perform well in the setting they were designed for, they often lack the ability to quickly adapt once operating conditions change drastically. Of particular impor- tance is resilience to node and link failures, as clusters of nodes that lost their leader or split apart need to re-organize and find each other again. With Demos, we present a low- power wireless protocol that ensures robust network orches- tration despite such failures. Demos rapidly finds consen- sus on leadership with its cluster coordination mechanism even if the set of nodes fluctuates by introducing new elec- tion quorums. In addition, a novel cluster discovery scheme enables autonomous clusters to merge on the fly and max- imize network coverage. Experiments with controlled mo- bility on a multi-hop network of 24 nodes demonstrate that Demos maintains a reliable data exchange despite severe disruptions and adapts to changes within seconds. We fur- ther find that Demos’ ability to continuously coordinate and discover achieves highly robust orchestration of fully au- tonomous clusters.

December 2023 2023 IEEE Global Communications Conference Conference

Federated Deep Reinforcement Learning for Task Participation in Mobile Crowdsensing

Sumedh Dongare, Andrea Patricia Ortiz Jimenez, Anja Klein

BibTeX DOI: 10.1109/GLOBECOM54140.2023.10436786

Abstract

Mobile Crowdsensing (MCS) is a promising distributed sensing architecture that harnesses the power of sensors on mobile units (MUs) to perform sensing tasks. The MCS is a dynamic system in which the requirements of the sensing tasks, the MUs’ conditions and the available resources change over time. The performance of an MCS system depends on the selection of the MUs participating in each sensing task. However, this is not a trivial problem. An optimal task participation strategy requires non-causal knowledge about the dynamic MCS system, a requirement that cannot be fulfilled in real implementations. Moreover, centralized optimization-based approaches do not scale with increasing number of participating MUs and often ignore the MUs’ preferences. To overcome these challenges, in this paper we propose a novel multi-agent federated deep reinforcement learning algorithm (FDRL-PPO) which does not need this perfect non-causal knowledge, but instead, enables the MUs to learn their own task participation strategies based on their own conditions, available resources, and preferences. Through federated learning, the MUs share their learned strategies without disclosing sensitive information, enabling a robust and scalable task participation scheme. Numerical evaluations validate the effectiveness and efficiency of FDRL-PPO in comparison with reference schemes.

December 2023 2023 IEEE Global Communications Conference Conference

Robust Dynamic Trajectory Optimization for UAV-Aided Localization of Ground Target

Lin Xiang, Mengshuai Zhang, Anja Klein

PDF BibTeX DOI: 10.1109/GLOBECOM54140.2023.10437337

Abstract

In this paper, we consider employing an unmanned aerial vehicle (UAV) equipped with an onboard radar transceiver to localize a ground target at an unknown position. Exploiting the UAV’s mobility, we aim to gather line-of-sight (LoS) range measurements from favorable waypoints and improve the ensuing multi-lateration process while estimating the target’s location. To this end, we introduce a novel localization error metric, characterized geometrically by the radius of a defined confidence region where the target resides at a predetermined confidence level. Additionally, we investigate robust dynamic optimization of the UAV’s trajectory to minimize the defined localization error metric online, utilizing sequentially available but delayed range estimates. The formulated optimization problem belongs to a convex-nonconcave minimax problem, which is generally intractable. To solve this problem, we further propose two iterative online algorithms based on semidefinite programming (SDP) relaxation and alternating/sequential convex optimization techniques. Simulation results show that the proposed online schemes outperform several benchmarks, either in the final localization accuracy or in the rate of decreasing the localization error.

December 2023 2023 IEEE Global Communications Conference Conference

Energy-Constrained UAV Trajectory Design for Sensing Information Maximization

Yi Wang, Lin Xiang, Anja Klein

BibTeX

December 2023 2023 IEEE Global Communications Conference Conference

A Unified Approach to Learn Transmission Strategies Using Age-Based Metrics in Point-to-Point Wireless Communication

Wanja De Sombre, Felipe Marques, Friedrich Pyttel, Andrea Patricia Ortiz Jimenez, Anja Klein

BibTeX

Abstract

Based on the Age of Information as an optimization criterion, proposals for further age-based metrics have been made in recent years in the Internet of Things (IoT) domain. The research community’s great interest in age-based metrics for point-to-point wireless communication has led to a multitude of different scenarios being investigated, including energy opti- mization, sensing, and risk-sensitivity. All these scenarios involve a sender-receiver pair and revolve around finding appropriate times for the sender to communicate status updates to the receiver. We propose a unified and modular framework that represents the aforementioned options in various combinations and enables transferring solutions developed for specific cases to a variety of scenarios. We generalize an existing optimization approach, which decides to transmit based on a threshold for the age-based metric, using this framework. We develop a unified and extended Q-learning-based algorithm with mechanisms to learn suitable solutions for all scenarios derived from our framework. These mechanisms accelerate the learning process and result in improved algorithmic performance compared to traditional Q-learning. Furthermore, we demonstrate the effectiveness of our solution in numerical simulations. Our unified solution outperforms several reference schemes in terms of age-based metrics, energy consumption, and risk. We present our findings as a starting point to investigate transmission strategies for more general settings with a more efficient approach.

December 2023 22nd IEEE International Symposium on Mixed and Augmented Reality (ISMAR 2023 ) Conference

“Can You Handle the Truth?”: Investigating the Effects of AR-Based Visualization of the Uncertainty of Deep Learning Models on Users of Autonomous Vehicles

Achref Doula, Lennart Schmidt, Max Mühlhäuser, Alejandro Sanchez Guinea

BibTeX DOI: 10.1109/ismar59233.2023.00040

Abstract

The recent advances in deep learning have paved the way for autonomous vehicles (AVs) to take charge of more complex tasks in the navigation process. However, predictions of deep learning models are subject to different types of uncertainty that may put the user and the surrounding environment in danger. In this paper, we investigate the effects that AR-based visualizations of 3 types of uncertainties in deep learning modules for path planning in AVs may have on drivers. The uncertainty types of the deep learning models that we consider are: the waypoint uncertainty, the situation uncertainty, and the path uncertainty. We propose 3 concepts to visualize the 3 uncertainty types on a Windshield display. We evaluate our AR-based concepts with a user study (N=20) using a VR-based immersive environment, to ensure the security of the participants. The results of our evaluation reveal that the absence of uncertainty visualization leads to lower driver engagement. More importantly, the combination of situation uncertainty and path uncertainty visualizations leads to higher driver engagement, and higher trust in the automated vehicle, while inducing an acceptable mental load for the drive.

December 2023 2023 IEEE Global Communications Conference Conference

Contextual Multi-Armed Bandits for Non-Stationary Heterogeneous Mobile Edge Computing

Maximilian Wirth, Andrea Patricia Ortiz Jimenez, Anja Klein

PDF BibTeX DOI: 10.1109/GLOBECOM54140.2023.10437572

Abstract

Base station (BS) selection for task offloading in Mobile Edge Computing (MEC) is a challenging problem due to the dynamic nature of MEC systems. The wireless channel as well as the load of BSs are stochastic quantities that can change in a statistically non-stationary fashion. Moreover, the computation capabilities of the BSs are heterogeneous. As the dynamic behaviour of a MEC system is, in practical scenarios, not known in advance, deciding where to offload has to be done under uncertainty about the MEC system and considering its non-stationary and heterogeneous characteristics. This paper in- vestigates latency minimization in MEC with heterogeneous BSs. In order to meet low latency demands, a mobile unit (MU) has to quickly identify the best BS for offloading different computation tasks while facing uncertainty about the non-stationary system dynamics. To solve this problem, we propose a novel piece-wise stationary contextual Multi-Armed Bandit (MAB) algorithm that treats different task types as context and detects non-stationary changes in the BSs’ performance. With the use of extensive simulations, we show that our proposed approach outperforms state-of-the-art algorithms, as it quickly adapts to changes in the MEC system and exhibits no penalty during stationary phases.

December 2023 Journal of Information Security and Applications Article

From the detection towards a pyramidal classification of terrorist propaganda

Andrea Tundis, Ahmed Ali Shams, Max Mühlhäuser

PDF BibTeX DOI: 10.1016/j.jisa.2023.103646

Abstract

With over 3,81 billion users from across the world, social media platforms provide a borderless environment for people of different nationalities, races, ethnicities, and religious beliefs to interact and communicate with each other. Not only for legitimate purposes are these digital tools used, but also groups of extremists and terrorist organizations take advantage of the features of these platforms to spread radicalization, propaganda, brainwashing and for online recruitment. Recent studies, conducted in this area, are trying to face with this phenomenon, but due to the heterogeneity of the sources, the large amount of daily data generated, and especially the different levels of radicalization of the users, make it even more difficult to define effective and general countermeasures against such phenomenon. In this context, the paper provides a solution that not only aims to support the detection of terrorist propaganda (and related users), but also to support its further categorization, centered on a pyramid classification model, by analyzing the level of users’ radicalization. This approach has two fundamental complementary advantages, as on the one hand it enables the establishment of priorities in terms of intervention, and on the other hand to define and apply targeted countermeasures based on the level of user’s radicalization. The proposed model and the results obtained from its experimentation are shown and discussed in comparison to previous works.

November 2023 2023 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR’23) Conference

Hector UI: A Flexible Human-Robot User Interface for (Semi-)Autonomous Rescue and Inspection Robots

Stefan Fabian, Oskar von Stryk

PDF BibTeX DOI: 10.1109/SSRR59696.2023.10499954

Abstract

The remote human operator’s user interface (UI) is an important link to make the robot an efficient extension of the operator’s perception and action. In rescue applications, several studies have investigated the design of operator interfaces based on observations during major robotics competitions or field deployments. Based on this research, guidelines for good interface design were empirically identified. The investigations on the UIs of teams participating in competitions are often based on external observations during UI application, which may miss some relevant requirements for UI flexibility. In this work, we present an open-source and flexibly configurable user interface based on established guidelines and its exemplary use for wheeled, tracked, and walking robots. We explain the design decisions and cover the insights we have gained during its highly successful applications in multiple robotics competitions and evaluations. The presented UI can also be adapted for other robots with little effort and is available as open source.

November 2023 2023 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR’23) Conference

Affordance-based Actionable Semantic Mapping and Planning for Mobile Rescue Robots

Frederik Bark, Kevin Daun, Oskar von Stryk

PDF BibTeX DOI: 10.1109/SSRR59696.2023.10499938

Abstract

Autonomous and tele-operation of rescue robots in urban search and rescue (USAR) environments is very challenging as details of missions and environments are usually unknown, mission goals might change dynamically and there is only little repeatability between different missions. Therefore, we propose a novel actionable semantic mapping and planning approach which leverages complementary capabilities of operator and robotic assistance functions. While related methods often focus on accuracy for geometric or semantic representations, we propose a novel framework focusing on an actionable map representation which is well suited for planning complex behaviors in uncertain environments. We represent the environment topologically as a scene graph coupled with a geometrically and semantically dense representation as Truncated Signed Distance Functions. We propose to apply the concept of affordances to map possible actions and costs to object classes. Building on that, we propose a combined topological and geometric task planning method allowing for easy operator interaction on task selection and prioritization. The successful application in two complex scenarios demonstrates the flexibility and efficiency of the proposed approach and the benefit of operator interaction.

November 2023 2023 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR’23) Conference

Requirements and challenges for autonomy and assistance functions for ground rescue robots in reconnaissance missions

Kevin Daun, Oskar von Stryk

BibTeX DOI: 10.1109/SSRR59696.2023.10499930

Abstract

While rescue robots are becoming more established as part of disaster response, they are typically teleoperated in actual disasters. (Autonomous) assistance functions can improve performance, extend functionality and reduce operator overload. It is necessary to understand relevant requirements to ensure that developed capabilities apply to real-world needs. Previous analyses focused on general aspects of rescue robots, leaving a gap in understanding requirements for (autonomous) assistance functions. We address this gap and provide a detailed, evidence-driven analysis of application requirements and research challenges for (autonomous) assistance functions for rescue robots in reconnaissance missions. We base our analysis on a comprehensive model for technology acceptance and consider reports of past deployments, related analyses, our own experience from deploying robots, and insights from workshops with first responders. We define relevant aspects of an integrated function capability and analyze general and specific requirements for assistance functions and autonomy. We relate our results with current assistance functions and identify several research challenges. A key insight is the need for an increased research focus on novel approaches combining the complementary capabilities of human operators and robotic assistance functions.

November 2023 2023 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR’23) Conference

Online 2D-3D Radiation Mapping and Source Localization using Gaussian Processes with Mobile Ground Robots

Jonas Süß, Martin Volz, Kevin Daun, Oskar von Stryk

BibTeX DOI: 10.1109/SSRR59696.2023.10499940

Abstract

We present a novel method for online radiation mapping and source localization in 2D and 3D with mobile ground robots using Gaussian Processes to assist personnel in potentially dangerous scenarios such as nuclear catastrophes or dismantling nuclear reactors. While existing methods typically make strong model assumptions or are limited for robot onboard application by high computational cost, we propose a method that requires only weak model assumptions and gains efficiency by pre-sampling and local map update schemes. The resulting models can predict the radiation levels in complex indoor environments with multiple sources and quantify the uncertainty in their estimates. The proposed method can be applied in combination with teleoperated, semi-autonomous, or autonomous exploration. It was successfully evaluated at the EnRicH 2023 competition in a decommissioned nuclear power plant, where it provided the best localization and mapping of five radiation sources and received the award for radiation mapping. Our evaluation of data from the competition validates the accuracy and computational efficiency of the proposed approach. Moreover, we provide an open-source ROS implementation of the proposed method and open-access evaluation data.

November 2023 Water Research Article

Do resilience metrics of water distribution systems really assess resilience? A critical review

Michaela Leštáková, Kevin Logan, Imke-Sophie Rehm, Peter F. Pelz, John Friesen

PDF BibTeX DOI: 10.1016/j.watres.2023.120820

November 2023 IEEE Communications Letters Article

Glucose Regulation Through Cooperative Molecular Communication

Theodoros M. Theodoridis, Sotiris A. Tegos, Panagiotis D. Diamantoulakis, Vahid Jamali, George K. Karagiannidis

BibTeX DOI: 10.1109/LCOMM.2023.3315012

Abstract

In Type 1 diabetes, the pancreatic beta cells responsible for producing insulin are destroyed by the immune system. Insulin is needed to activate an insulin-dependent glucose transporter, which is responsible for taking glucose into the muscle cell for metabolism. Recent advances in nanotechnology, bioengineering and synthetic biology are bringing the artificial beta cell (ABC) closer to reality. In this letter, we model glucose regulation by ABCs as a cooperative molecular communication system, in which the glucose source is seen as the transmitter and the muscle as the receiver. the last absorbs the glucose in the presence of insulin, and the ABC is modeled as a a decode-and-forward relay that detects glucose molecules and releases insulin in response. Using this model, we analyze the end-to-end system performance for ABC-assisted glucose regulation by providing closed-form expressions for the probabilities of hyperglycemia and hypoglycemia and the error probability of the system. In addition, we present simulation results for quantifying performance and validation of the analysis.

November 2023 2023 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR’23) Conference

Accurate Pose Prediction on Signed Distance Fields for Mobile Ground Robots in Rough Terrain

Martin Oehler, Oskar von Stryk

PDF BibTeX DOI: 10.1109/SSRR59696.2023.10499944

Abstract

Autonomous locomotion for mobile ground robots in unstructured environments such as waypoint navigation or flipper control requires a sufficiently accurate prediction of the robot-terrain interaction. Heuristics like occupancy grids or traversability maps are widely used but limit actions available to robots with active flippers as joint positions are not taken into account. We present a novel iterative geometric method to predict the 3D pose of mobile ground robots with active flippers on uneven ground with high accuracy and online planning capabilities. This is achieved by utilizing the ability of signed distance fields to represent surfaces with sub-voxel accuracy. The effectiveness of the presented approach is demonstrated on two different tracked robots in simulation and on a real platform. Compared to a tracking system as ground truth, our method predicts the robot position and orientation with an average accuracy of 3.11 cm and 3.91°, outperforming a recent heightmap-based approach. The implementation is made available as an open-source ROS package.

November 2023 31st European Signal Processing Conference (EUSIPCO) Conference

Accelerated Sample-Accurate R-Peak Detectors Based on Visibility Graphs

Jonas Emrich, Taulant Koka, Sebastian Wirth, Michael Muma

BibTeX DOI: 10.23919/EUSIPCO58844.2023.10290007

Abstract

The effective detection and accurate clinical diagnosis of cardiac conditions strongly relies on the correct localization of R-peaks in the electrocardiogram (ECG). Recently, demand for sample-accurate R-peak detection, which is essential to precisely reveal vital features, such as heart rate variability and pulse transit time, has increased. Therefore, we propose two novel sample-accurate visibility-graph-based R-peak detectors, the FastNVG and the FastWHVG detector. The visibility graph (VG) transformation maps a discrete signal into a graph by representing sampling locations as nodes and establishing edges between mutually visible samples. However, processing large-scale clinical ECG data urgently demands further acceleration of VG-based algorithms. The proposed methods reduce the required computation time by one order of magnitude and simultaneously decrease the required memory compared to a recently proposed VG-based R-Peak detector. Instead of transforming the entire ECG, the proposed acceleration benefits largely from building the VG based on a subset containing only the samples relevant to R-peak detection. Further acceleration is obtained by adopting the computationally efficient horizontal visibility graph, which has not yet been used for R-peak detection. Numerical experiments and benchmarks on multiple ECG databases demonstrate a significantly superior performance of the proposed VG-based methods compared to popular R-peak detectors.

November 2023 31st European Signal Processing Conference (EUSIPCO 2023) Conference

Sparsity-Aware Block Diagonal Representation for Subspace Clustering

Aylin Taştan, Michael Muma, Esa Ollila, Abdelhak M. Zoubir

BibTeX DOI: 10.23919/EUSIPCO58844.2023.10289969

Abstract

A block diagonally structured affinity matrix is an informative prior for subspace clustering which embeds the data points in a union of low-dimensional subspaces. Structuring a block diagonal matrix can be challenging due to the determination of an appropriate sparsity level, especially when outliers and heavy-tailed noise obscure the underlying subspaces. We propose a new sparsity-aware block diagonal representation (SABDR) method that robustly estimates the appropriate sparsity level by leveraging upon the geometrical analysis of the low-dimensional structure in spectral clustering. Specifically, we derive the Euclidean distance between the embeddings of different clusters to develop a computationally efficient density-based clustering algorithm. In this way, the sparsity parameter selection problem is re-formulated as a robust approximation of target between-clusters distances. Comprehensive experiments using real-world data demonstrate the effectiveness of SABDR in different subspace clustering applications.

October 2023 Fachtagung Katastrophenforschung 2023 Conference

Infrastrukturen und historisches Wissen: Eine interdisziplinäre Analyse der Resilienz von Schienen- und Kabelnetzen.

Marco Zivkovic, Jonas Franken, Nadja Thiessen, Jens Ivo Engels, Christian Reuter

PDF BibTeX

October 2023 Conference on Communications and Network Security (CNS 2023) Conference

BeamSec: A Practical mmWave Physical Layer Security Scheme Against Strong Adversaries

Afifa Ishtiaq, Arash Asadi, Ladan Khaloopour, Waqar Ahmed, Vahid Jamali, Matthias Hollick

BibTeX DOI: 10.1109/CNS59707.2023.10289003

Abstract

The high directionality of millimeter-wave (mmWave) communication systems has proven effective in reducing the attack surface against eavesdropping, thus improving the physical layer security. However, even with highly directional beams, the system is still exposed to eavesdropping against adversaries located within the main lobe. In this paper, we propose BeamSec, a solution to protect the users even from adversaries located in the main lobe. The key feature of BeamSec are: (i) Operating without the knowledge of eavesdropper’s location/channel; (ii) Robustness against colluding eavesdropping attack and (iii) Standard compatibility, which we prove using experiments via our IEEE 802.11ad/ay-compatible 60 GHz phased-array testbed. Methodologically, BeamSec first identifies uncorrelated and diverse beampairs between the transmitter and receiver by analyzing signal characteristics available through standard-compliant procedures. Next, it encodes the information jointly over all selected beampairs to minimize information leakage. We study two methods for allocating transmission time among different beams, namely uniform allocation (no knowledge of the wireless channel) and optimal allocation for maximization of the secrecy rate (with partial knowledge of the wireless channel). Our experiments show that BeamSec outperforms the benchmark schemes against single and colluding eavesdroppers and enhances the secrecy rate by 79.8% over a random paths selection benchmark.

October 2023 ICC 2023 - IEEE International Conference on Communications Conference

Deterministic Identification for MC ISI-Poisson Channel

Mohammad J. Salariseddigh, Vahid Jamali, Uzi Pereg, Holger Boche, Christian Deppe, Robert Schober

BibTeX DOI: 10.1109/ICC45041.2023.10278856

Abstract

Several applications of molecular communications (MC) feature an alarm-prompt behavior for which the prevalent Shannon capacity may not be the appropriate performance metric. The identification capacity as an alternative measure for such systems has been motivated and established in the literature. In this paper, we study deterministic identification (DI) for the discrete-time Poisson channel (DTPC) with intersymbol interference (ISI) where the transmitter is restricted to an average and a peak molecule release rate constraint. Such a channel serves as a model for diffusive MC systems featuring long channel impulse responses and employing molecule counting receivers. We derive lower and upper bounds on the DI capacity of the DTPC with ISI when the number of ISI channel taps K may grow with the codeword length n (e.g., due to increasing symbol rate). As a key finding, we establish that for deterministic encoding, the codebook size scales as 2(nlogn)R assuming that the number of ISI channel taps scales as K=2κlogn , where R is the coding rate and κ is the ISI rate. Moreover, we show that optimizing κ leads to an effective identification rate [bits/s] that scales linearly with n , which is in contrast to the typical transmission rate [bits/s] that is independent of n .

October 2023 2023 IEEE International Conference on Communications Workshop Conference

Risk-Sensitive Optimization and Learning for Minimizing Age of Information in Point-to-Point Wireless Communications

Wanja De Sombre, Andrea Patricia Ortiz Jimenez, Frank Aurzada, Anja Klein

BibTeX DOI: 10.1109/ICCWorkshops57953.2023.10283567

Abstract

When using Internet of Things (IoT) networks for monitoring, devices rely on fresh status updates about the monitored process. To measure the freshness of these status updates, the concept of Age of Information (AoI) is used. However, critical applications, e.g., those involving human safety, require not only fresh updates, but also a low risk of experiencing high AoI values. In this work, we introduce the notion of risky states for these high AoI events. We consider a point-to-point wireless communication scenario containing a sender transmitting randomly arriving status updates to a receiver through a wireless channel. The sender decides, when to send a status update and when to wait for a newer one. The sender’s goal is to jointly minimize the AoI at the receiver, the required transmission energy and the frequency of visiting risky states. We present two solutions for this problem using optimization and learning, respectively For the optimization approach, we propose a family of threshold-based transmission strategies, which trigger a transmission whenever the difference between the AoI at the sender and at the receiver exceeds a certain threshold. Our proposed learning approach directly includes our notion of risky states into traditional Q -learning As a result, it balances the minimization of AoI and the required transmission energy, with the frequency of visiting risky states. Through numerical results, we show that our proposed risk-aware approaches outperform relevant reference schemes. Moreover, and in contrast to value iteration, their computational complexity does not depend on the set of possible AoI values.

October 2023 58th International Conference on Communications Conference

Online Learning in Matching Games for Task Offloading in Multi-Access Edge Computing

Helena Mehler, Bernd Simon, Anja Klein

BibTeX DOI: 10.1109/ICC45041.2023.10279031

Abstract

In multi-access edge computing (MEC), mobile users (MUs) can offload computation tasks to nearby computational resources, which are owned by a mobile network operator (MNO), to save energy. In this work, we investigate two important challenges of task offloading in MEC: (i) The techno-economic interactions of the MNO and the MUs. The MNO faces a profit maximization problem, whereas the MUs face an energy minimization problem. (ii) Limited information at the MUs about the MNO’s communication and computation resources and the task offloading strategies of other MUs. To overcome these challenges, we model the task offloading problem as a matching game between the MUs and the MNO including their techno-economic interactions. Furthermore, we propose a novel Collision-Avoidance Task Offloading Multi-Armed-Bandit (CA-TO-MAB) algorithm, that allows the MUs to learn the amount of available resources at the MNO and the task offloading strategies of other MUs in an online, fully decentralized way. We show that by using CA-TO-MAB, the cumulative revenue of the MNO can be increased by 25% and, at the same time the energy consumption of the MUs can be reduced by 6% compared to state-of-the-art online learning algorithms for task offloading. Furthermore, the communication overhead can be reduced by 55% compared to a non-learning game-theoretic approach.

October 2023 IEEE International Conference on Communication Conference

Completion Time Minimization for UAV-Based Communications with a Finite Buffer

Yi Wang, Lin Xiang, Anja Klein

BibTeX DOI: 10.1109/ICC45041.2023.10278804

Abstract

This paper considers a buffer-aided unmanned aerial vehicle (UAV) serving as an aerial relay for communication between a base station (BS) and multiple ground users (GUs). Thanks to its flexible mobility, the UAV can achieve high-rate communications with the GUs/BS by buffering the communication data and exploiting the favorable channel conditions on its flight trajectory for transmission and reception. However, the size of the buffer is limited in practice, which may severely restrict the throughput gains enabled by buffering. Whether it is beneficial to consider buffering at UAVs with a small buffer is an open research problem, which is tackled in this paper. Assuming a finite buffer mounted at the UAV, we consider joint optimization of the resource allocation, data buffering, and trajectory planning for minimizing the UAV’s completion time required for delivering a given data volume from each GU to the BS, where the resource allocation contains power and bandwidth allocation. The formulated optimization problem is a mixed-integer nonconvex program, which is generally intractable. To solve this problem, we propose a novel low-complexity two-layer iterative suboptimal algorithm based on bisection search and penalty successive convex approximation (PSCA). Note that minimizing the completion time in turn maximizes the average throughput, i.e., the amount of data delivered from the GUs to the BS per unit of time. Simulation results show that the buffer with sufficiently large size can increase the UAV’s average throughput by up to 123.8% compared to without buffering. Moreover, with our proposed scheme, 63.2% of the throughput gains can already be achieved using only a small buffer.

October 2023 Weizenbaum Journal of the Digital Society Article

Digital Volunteers during the COVID-19 Pandemic: Care Work on Social Media for Socio-technical Resilience

Stefka Schmid, Laura Gianna Guntrum, Steffen Haesler, Lisa Schultheiß, Christian Reuter

BibTeX DOI: 10.34669/WI.WJDS/3.3.6

Abstract

Like past crises, the COVID-19 pandemic has galvanized individual volunteers to contribute to the public response. This includes digital volunteers who have organized physical aid and conducted social media activities. Analyzing German volunteering support groups on Facebook and related Reddit threads in the context of COVID-19, we show what types of help are offered and how social media users interact with each other to cope with the situation. We reveal that most users offering help online mostly perform typical care work, such as buying groceries or giving advice. Crucially, volunteering is characterized by relationships of care. This means it builds on affirmative interactions. In spite of some misdirected offers and regressive interruptions, people use the possibility to make their voices heard and, showing empathy, help each other to live with the crisis. Social media like Facebook mediate societal structures, including relationships of care, offering a space for the continuous, cumulatively resilient conduct of care work. Reflecting on the traditional division of labor in crisis volunteering and counter-productive dynamics of care and empathy, we aim to articulate a feminist ethics of care that allows for interactions on social media that foster generative computer-supported collaboration.

October 2023 IEEE Transactions on Molecular, Biological and Multi-Scale Communications Article

Deterministic Identification for Molecular Communications Over the Poisson Channel

Mohammad Javad Salariseddigh, Vahid Jamali, Uzi Pereg, Holger Boche, Christian Deppe, Robert Schober

BibTeX DOI: 10.1109/TMBMC.2023.3324487

Abstract

Various applications of molecular communications (MC) are event-triggered, and, as a consequence, the prevalent Shannon capacity may not be the right measure for performance assessment. Thus, in this paper, we motivate and establish the identification capacity as an alternative metric. In particular, we study deterministic identification (DI) for the discrete-time Poisson channel (DTPC), subject to an average and a peak molecule release rate constraint, which serves as a model for MC systems employing molecule counting receivers. It is established that the number of different messages that can be reliably identified for this channel scales as 2(nlogn)R , where n and R are the codeword length and coding rate, respectively. Lower and upper bounds on the DI capacity of the DTPC are developed. The obtained large capacity of the DI channel sheds light on the performance of natural DI systems such as natural olfaction, which are known for their extremely large chemical discriminatory power in biology. Furthermore, numerical results for the empirical miss-identification and false identification error rates are provided for finite length codes. This allows us to characterize the behaviour of the error rate for increasing codeword lengths, which complements our theoretically-derived scale for asymptotically large codeword lengths.

October 2023 11th ACM Symposium on Spatial User Interaction Conference

DensingQueen: Exploration Methods for Spatial Dense Dynamic Data

Julius von Willich, Sebastian Günther, Andrii Matviienko, Martin Schmitz, Florian Müller, Max Mühlhäuser

PDF BibTeX DOI: 10.1145/3607822.3614535

Abstract

Research has proposed various interaction techniques to manage the occlusion of 3D data in Virtual Reality (VR), e.g., via gradual refinement. However, tracking dynamically moving data in a dense 3D environment poses the challenge of ever-changing occlusion, especially if motion carries relevant information, which is lost in still images. In this paper, we evaluated two interaction modalities for Spatial Dense Dynamic Data (SDDD), adapted from existing interaction methods for static and spatial data. We evaluated these modalities for exploring SDDD in VR, in an experiment with 18 participants. Furthermore, we investigated the influence of our interaction modalities on different levels of data density on the users’ performance in a no-knowledge task and a prior-knowledge task. Our results indicated significantly degraded performance for higher levels of density. Further, we found that our flashlight-inspired modality successfully improved tracking in SDDD, while a cutting plane-inspired approach was more suitable for highlighting static volumes of interest, particularly in such high-density environments.

October 2023 29th Annual International Conference on Mobile Computing and Networking (ACM MobiCom ‘23) Conference

Software-Defined Wireless Communication Systems for Heterogeneous Architectures

David Volz, Andreas Koch, Bastian Bloessl

BibTeX DOI: 10.1145/3570361.3614084

Abstract

Future cellular networks will be programmable and increasingly software-defined with APIs to hook into the communication stack closer and closer to the physical layer. This allows operators, for example, to plug-in third-party machine learning algorithms to optimize performance. At the same time, this flexibility implies that compute resources cannot be provisioned statically but have to be distributed dynamically during runtime. While the hardware platforms for such systems are available, we lack suitable software frameworks that help to realize such systems. In this demonstration, we present two Open Source projects that fill this gap: FutureSDR, a portable real-time signal processing framework with native support for accelerators (like GPUs and FGPAs); and IPEC, which enables fully automatic composition of multi-accelerator FPGA designs. We believe that these tools - especially in combination with each other - can provide the base for building research prototypes and allow experimentation with software-defined wireless communication systems.

October 2023 29th Annual International Conference on Mobile Computing and Networking (ACM MobiCom ‘23) Conference

Introducing FreeSpeaker - A Modular Smart Home Hub Prototyping Platform

Hermann Leinweber, Jonatan Crystall, Frank Hessel, Florentin Putz, Matthias Hollick

PDF BibTeX DOI: 10.1145/3570361.3614080

Abstract

Smart home speakers have become a commodity item in many households and provide interesting research opportunities in areas like wireless communication and human-computer interaction. Commercial devices do not provide sufficient access for many research tasks. We present a modular smart home hub designed specifically for research purposes. The electronic and mechanical components are designed with reproducibility in mind and can be easily recombined for a project’s needs. Additionally, we show applications of the hub in different scenarios.

September 2023 31st Interdisciplinary Information Management Talks Conference

A Large-Scale Data Collection and Evaluation Framework for Android Device Security Attributes

Ernst Leierzopf, Michael Roland, René Mayrhofer, Florentin Putz

PDF BibTeX DOI: 10.35011/IDIMT-2023-63

Abstract

Android’s fast-lived development cycles and increasing amounts of manufacturers and device models make a comparison of relevant security attributes, in addition to the already difficult comparison of features, more challenging. Most smartphone reviews only consider offered features in their analysis. Smartphone manufacturers include their own software on top of the Android Open Source Project (AOSP) to improve user experience, to add their own pre-installed apps or apps from third-party sponsors, and to distinguish themselves from their competitors. These changes affect the security of smartphones. It is insufficient to validate device security state only based on measured data from real devices for a complete assessment. Promised major version releases, security updates, security update schedules of devices, and correct claims on security and privacy of pre-installed software are some aspects, which need statistically significant amounts of data to evaluate. Lack of software and security updates is a common reason for shorter lifespans of electronics, especially for smartphones. Validating the claims of manufacturers and publishing the results creates incentives towards more sustainable maintenance and longevity of smartphones. We present a novel scalable data collection and evaluation framework, which includes multiple sources of data like dedicated device farms, crowdsourcing, and webscraping. Our solution improves the comparability of devices based on their security attributes by providing measurements from real devices.

September 2023 48th Conference on Local Computer Networks Conference

Energy-efficient Broadcast Trees for Decentralized Data Dissemination in Wireless Networks

Artur Sterz, Robin Klose, Markus Sommer, Jonas Höchst, Jakob Link, Bernd Simon, Anja Klein, Matthias Hollick, Bernd Freisleben

PDF BibTeX DOI: 10.1109/LCN58197.2023.10223400

Abstract

We present a novel multi-hop data dissemination protocol for wireless networks that minimizes the total energy consumption across an entire network by minimizing the transmission power at each hop. It is based on a game-theoretic model, constructs a spanning tree topology in a decentralized manner, and is usable in practice. We evaluate the protocol via simulation and a pratical implementation on a testbed of 75 Raspberry Pis, demonstrating that a total energy reduction of up to 90% can be achieved compared to a simple broadcast protocol.

September 2023 48th Conference on Local Computer Networks Conference

UAV Swarms for Joint Data Ferrying and Dynamic Cell Coverage via Optimal Transport Descent and Quadratic Assignment

Kai Cui, Lars Baumgärtner, Mustafa Burak Yilmaz, Mengguang Li, Christian Fabian, Benjamin Becker, Lin Xiang, Maximilian Bauer, Heinz Koeppl

BibTeX DOI: 10.1109/LCN58197.2023.10223388

Abstract

Both data ferrying with disruption-tolerant networking (DTN) and mobile cellular base stations constitute important techniques for UAV-aided communication in situations of crises where standard communication infrastructure is unavailable. For optimal use of a limited number of UAVs, we propose providing both DTN and a cellular base station on each UAV. Here, DTN is used for large amounts of low-priority data, while capacity-constrained cell coverage remains reserved for emergency calls or command and control. We optimize cell coverage via a novel optimal transport-based formulation using alternating minimization, while for data ferrying we periodically deliver data between dynamic clusters by solving quadratic assignment problems. In our evaluation, we consider different scenarios with varying mobility models and a wide range of flight patterns. Overall, we tractably achieve optimal cell coverage under quality-of-service costs with DTN-based data ferrying, enabling large-scale deployment of UAV swarms for crisis communication.

September 2023 2023 IEEE/CIC International Conference on Communications in China Conference

Risk-aware Online Optimization of Cross-Layer Resource Allocation in Next-Generation WLANs

Jing Zhang, Jinke Zheng, Lin Xiang, Xiaohu Ge

BibTeX DOI: 10.1109/ICCC57788.2023.10233666

Abstract

Fiber-to-the-room (FTTR) technology provides a promising solution to enable high-capacity indoor wireless communications in next-generation wireless local area networks (WLANs). In this paper, we consider online cross-layer resource allocation in the uplink of FTTR WLANs to facilitate immersive user experience. We formulate a nonconvex optimization problem for maximizing the long-term total network utility while ensuring proportional fairness among the users and guaranteeing users’ quality of service (QoS) requirements. Unlike the existing cross-layer resource allocation schemes, our approach simultaneously ensures queue stability and mitigates the risk of data loss caused by e.g. fading and interference, preemptive multiuser access, and finite buffer capacity, both achieved by limiting the entropic value-at-risk (EVaR) of the users’ data queues. By employing Lyapunov optimization and relaxation techniques, we further transform the NP-hard problem into a convex problem and solve it via convex optimization. Simulation results show that the proposed EVaR-based cross-layer resource allocation framework can effectively limit the length of data queues at the users and, at the same time, provide fairness for the throughput achieved among users.

September 2023 22nd International Conference on Modeling and Applied Simulation Conference

Combining TAPAS and SUMO towards crises management based on traffic data

María López Díaz, Andrea Tundis

PDF BibTeX DOI: 10.46354/i3m.2023.mas.005

Abstract

Human-made crisis and natural disasters are major concerns for the society, as they can put in a risk the life of people. Especially in urban area, which are typically highly crowded, the occurrence of a situation of dangers the consequent change of behaviors due to panics can be difficult to imagine, which affects the ability to be able to define appropriate countermeasures to mitigate the crisis itself. That’s why the use of digital solutions can be beneficial to support the analysis of such scenario and improve the understanding of the behavior in case of crisis. In this context, our paper focuses on the transportation infrastructures and proposes an integrated solution based on the combination of two simulation tools called TAPAS and SUMO to support the modelling and simulation of mobility scenario. The integrated solution is experimented in the context of city of Darmstadt (Germany) by simulating normal and change of behavior in terms of mobility and showing the deriving benefits.

August 2023 42nd IEEE Conference on Computer Communications (INFOCOM 2023) Conference

Safehaul: Risk-Averse Learning for Reliable mmWave Self-Backhauling in 6G Networks

Amir Ashtari Gargari, Andrea Patricia Ortiz Jimenez, Matteo Pagin, Anja Klein, Matthias Hollick, Michele Zorzi, Arash Asadi

BibTeX DOI: 10.1109/INFOCOM53939.2023.10228969

Abstract

Wireless backhauling at millimeter-wave frequencies (mmWave) in static scenarios is a well-established practice in cellular networks. However, highly directional and adaptive beamforming in today’s mmWave systems have opened new possibilities for self-backhauling. Tapping into this potential, 3GPP has standardized Integrated Access and Backhaul (IAB) allowing the same base station to serve both access and backhaul traffic. Although much more cost-effective and flexible, resource allocation and path selection in IAB mmWave networks is a formidable task. To date, prior works have addressed this challenge through a plethora of classic optimization and learning methods, generally optimizing a Key Performance Indicator (KPI) such as throughput, latency, and fairness, and little attention has been paid to the reliability of the KPI. We propose Safehaul, a risk-averse learning-based solution for IAB mmWave networks. In addition to optimizing average performance, Safehaul ensures reliability by minimizing the losses in the tail of the performance distribution. We develop a novel simulator and show via extensive simulations that Safehaul not only reduces the latency by up to 43.2% compared to the benchmarks, but also exhibits significantly more reliable performance, e.g., 71.4% less variance in achieved latency.

August 2023 2023 IEEE International Symposium on Information Theory (ISIT) Conference

Deterministic Identification for MC Binomial Channel

Mohammad Javad Salariseddigh, Vahid Jamali, Holger Boche, Christian Deppe, Robert Schober

BibTeX DOI: 10.1109/ISIT54713.2023.10206627

Abstract

The Binomial channel serves as a fundamental model for molecular communication (MC) systems employing molecule-counting receivers. Here, deterministic identification (DI) is addressed for the discrete-time Binomial channels (DTBC), subject to an average and a peak constraint on the molecule release rate. We establish that the number of different messages that can be reliably identified for the DTBC scales as 2 (n log n)R , where n and R are the codeword length and coding rate, respectively. Lower and upper bounds on the DI capacity of the DTBC are developed.

August 2023 97th Vehicular Technology Conference Conference

Full-Link AoI Analysis of Uplink Transmission in Next-Generation FTTR WLAN

Jing Zhang, Jing Liu, Lin Xiang, Xiaohu Ge

BibTeX DOI: 10.1109/VTC2023-Spring57618.2023.10200633

Abstract

Fiber-to-the-room (FTTR) wireless local area networks (WLANs) are a promising sixth-generation (6G) technology for extreme broadband low-latency indoor wireless communications. With dense deployment of access points (APs), namely optical network units (ONUs), and efficient spatial frequency reuse across the ONUs, FTTR WLANs enable the mobile devices to flexibly access any ONU in its communication range and reduce the collisions during data packet transmissions. However, FTTR WLANs share a passive optical network (PON) for time division multiplexing (TDM) based backhauling, which may incur long delays for scheduling packet transmissions over the PON. In this paper, the full-link age of information (FL-AoI) is proposed as a new performance metric to analyze the timeliness of indoor communications in FTTR WLANs, taking into account both the carrier sense multiple access with collision avoidance (CSMA/CA) based wireless transmission and the TDM based packet scheduling over the PON. The FL-AoI of FTTR WLANs is analyzed using stochastic geometry and stretched exponential path-loss (SEPL) based indoor wireless channel model. We show that rather than accessing the nearest ONUs, the FTTR WLAN also enable the mobile devices to access further ONUs to reduce the average FL-AoI. Meanwhile, there exists an optimal transmission distance to achieve minimal average FL-AoI in the FTTR WLAN, whose value depends on the deployment density of ONUs.

August 2023 22nd IEEE Statistical Signal Processing Workshop Conference

False Discovery Rate Control for Fast Screening of Large-Scale Genomics Biobanks

Jasin Machkour, Michael Muma, Daniel P. Palomar

BibTeX DOI: 10.1109/SSP53291.2023.10207957

Abstract

Genomics biobanks are information treasure troves with thousands of phenotypes (e.g., diseases, traits) and millions of single nucleotide polymorphisms (SNPs). The development of methodologies that provide reproducible discoveries is essential for the understanding of complex diseases and precision drug development. Without statistical reproducibility guarantees, valuable efforts are spent on researching false positives. Therefore, scalable multivariate and high-dimensional false discovery rate (FDR)-controlling variable selection methods are urgently needed, especially, for complex polygenic diseases and traits. In this work, we propose the Screen-T-Rex selector, a fast FDR-controlling method based on the recently developed T-Rex selector. The method is tailored to screening large-scale biobanks and it does not require choosing additional parameters (sparsity parameter, target FDR level, etc). Numerical simulations and a real-world HIV-1 drug resistance example demonstrate that the performance of the Screen-T-Rex selector is superior, and its computation time is multiple orders of magnitude lower compared to current benchmark knockoff methods.

July 2023 IEEE Intelligent Vehicles Symposium (IV 2023) Conference

Towards Continual Knowledge Learning of Vehicle CAN-data

Sajeel Ahmed, Ousama Esbel, Max Mühlhäuser, Alejandro Sanchez Guinea

BibTeX DOI: 10.1109/iv55152.2023.10186715

Abstract

In this paper, we propose a continual learning (CL) approach that adapts to the vehicle CAN-data flexibly and continuously. Our approach is capable of learning from vehicle CAN-bus data in multiple driving scenarios, adapting to the various drifts within each driving scenario. The basis for our approach corresponds to a common solver model and a series of supervisor models. Our solver model extends the memory-aware synapses approach with the use of weight cloning and weighted experience replay. Our supervisor model selects the output of the solver model that corresponds to the driving scenario present at the input. We evaluate our approach using a Tesla Model 3 CAN-data and 8 different driving scenarios. Our evaluation results show that our approach effectively learns multiple driving scenarios sequentially without forgetting the previous knowledge.

July 2023 2023 Designing Interactive Systems Conference Conference

Getting the Residents’ Attention: The Perception of Warning Channels in Smart Home Warning Systems

Steffen Haesler, Marc Wendelborn, Christian Reuter

BibTeX DOI: 10.1145/3563657.3596076

Abstract

About half a billion households are expected to use smart home systems by 2025. Although many IoT sensors, such as smoke detectors or security cameras, are available and governmental crisis warning systems are in place, little is known about how to warn appropriately in smart home environments. We created a Raspberry Pi based prototype with a speaker, a display, and a connected smart light bulb. Together with a focus group, we developed a taxonomy for warning messages in smart home environments, dividing them into five classes with different stimuli. We evaluated the taxonomy using the Experience Sampling Method (ESM) in a field study at participants’ (N = 13) homes testing 331 warnings. The results show that taxonomy-based warning stimuli are perceived to be appropriate and participants could imagine using such a warning system. We propose a deeper integration of warning capabilities into smart home environments to enhance the safety of citizens.

July 2023 24th International Conference on Digital Signal Processing Conference

Radar Based Humans Localization with Compressed Sensing and Sparse Reconstruction

Christian Eckrich, Christian A. Schroth, Vahid Jamali, Abdelhak M. Zoubir

BibTeX DOI: 10.1109/DSP58604.2023.10167990

Abstract

Localization and detection is a vital task in emergency rescue operations. Devastating natural disasters can create environments that are inaccessible or dangerous for human rescuers. Contaminated areas or buildings in danger of collapsing can be searched by rescue robots which are equipped with diverse sensors such as optical and radar sensors. In scenarios where the line of sight is blocked, e.g., by a wall, a door or heavy smoke or dust, sensors like LiDAR or cameras are not able to provide sufficient information. The usage of radar in these kinds of situations can drastically improve situational awareness and hence the likelihood of rescue. In this paper, we present a method that is used for radar imaging behind obstacles by utilizing a signal model that includes the floor reflection propagation path in addition to the direct path of the radar signal. Additionally, compressed sensing methods are presented and applied to real world radar data that was recorded by a Stepped Frequency Continuous Wave (SFCW) radar mounted on a semi-autonomous robot. The results show an improved radar image that allows the clear identification of persons behind obstacles.

July 2023 24th International Conference on Digital Signal Processing Conference

Attacks on Robust Distributed Learning Schemes via Sensitivity Curve Maximization

Christian A. Schroth, Stefan Vlaski, Abdelhak M. Zoubir

BibTeX DOI: 10.1109/DSP58604.2023.10167919

Abstract

Distributed learning paradigms, such as federated or decentralized learning, allow a collection of agents to solve global learning and optimization problems through limited local interactions. Most such strategies rely on a mixture of local adaptation and aggregation steps, either among peers or at a central fusion center. Classically, aggregation in distributed learning is based on averaging, which is statistically efficient, but susceptible to attacks by even a small number of malicious agents. This observation has motivated a number of recent works, which develop robust aggregation schemes by employing robust variations of the mean. We present a new attack based on sensitivity curve maximization (SCM), and demonstrate that it is able to disrupt existing robust aggregation schemes by injecting small, but effective perturbations.

July 2023 40th IEEE International Conference on Robotics and Automation (ICRA2023) Conference

PointCloudLab: An Environment for 3D Point Cloud Annotation with Adapted Visual Aids and Levels of Immersion

Achref Doula, Tobias Güdelhöfer, Andrii Matviienko, Max Mühlhäuser, Alejandro Sanchez Guinea

BibTeX DOI: 10.1109/icra48891.2023.10160225

Abstract

The annotation of 3D point cloud datasets is an expensive and tedious task. To optimize the annotation process, recent works have proposed the use of environments with higher levels of immersion in combination with different types of visual aids. However, two problems remain unresolved. First, the proposed environments limit the user to a unique level of immersion and a fixed hardware setup. Second, their design overlooks the interaction effects between the level of immersion and the visual aids on the quality of the annotation process. To address these issues, we propose PointCloudLab, an environment for 3D point cloud annotation that allows the use of different levels of immersion that work in combination with visual aids. Using PointCloudLab, we conducted a controlled experiment (N=20) to investigate the effects of levels of immersion and visual aids on the annotation process. Our findings reveal that higher levels of immersion combined with object-based visual aids lead to a faster and more accurate annotation. Furthermore, we found significant interaction effects between the levels of immersion and the visual aids on the accuracy of the annotation.

July 2023 40th IEEE International Conference on Robotics and Automation (ICRA2023) Conference

Scalable Task-Driven Robotic Swarm Control via Collision Avoidance and Learning Mean-Field Control

K. Cui, M. Li, C. Fabian, H. Koeppl

BibTeX DOI: 10.1109/ICRA48891.2023.10161498

Abstract

In recent years, reinforcement learning and its multi-agent analogue have achieved great success in solving various complex control problems. However, multi-agent rein-forcement learning remains challenging both in its theoretical analysis and empirical design of algorithms, especially for large swarms of embodied robotic agents where a definitive toolchain remains part of active research. We use emerging state-of-the-art mean-field control techniques in order to convert many-agent swarm control into more classical single-agent control of distributions. This allows profiting from advances in single-agent reinforcement learning at the cost of assuming weak interaction between agents. However, the mean-field model is violated by the nature of real systems with embodied, physically colliding agents. Thus, we combine collision avoidance and learning of mean-field control into a unified framework for tractably designing intelligent robotic swarm behavior. On the theoretical side, we provide novel approximation guarantees for general mean-field control both in continuous spaces and with collision avoidance. On the practical side, we show that our approach outperforms multi-agent reinforcement learning and allows for decentralized open-loop application while avoiding collisions, both in simulation and real UAV swarms. Overall, we propose a framework for the design of swarm behavior that is both mathematically well-founded and practically useful, enabling the solution of otherwise intractable swarm problems.

July 2023 2023 American Control Conference Conference

Estimating Parameter Regions for Structured Parameter Tuning via Reduced Order Subsystem Models

Roland Schurig, Andreas Himmel, Amer Mešanović, Richard D. Braatz, Rolf Findeisen

BibTeX DOI: 10.23919/ACC55779.2023.10156542

Abstract

Many large-scale systems are composed of subsystems operated by decentralized controllers, which are fixed in their structure, yet have parameters to tune. Initial tuning or subsequent adjustments dof those parameters ue to varying operating conditions or changes in the network of interconnected systems, while ensuring stability, performance, and security, pose a challenging task due to the overall complexity and size. Subsystems may not be willing or allowed to expose detailed information for safety and privacy reasons. In some cases, a comprehensive system model might not be available for global tuning, or the resulting problem might be computationally infeasible. To enable meaningful global parameter tuning while allowing for data privacy and security, we propose that the subsystems themselves should provide reduced-order models. These models capture the parametric dependency of the subsystem dynamics on the controller parameters. Specifically, we present a method to construct a region in the subsystems’ parameter space in which the deviation of the subsystem and the reduced-order model stays below a specified error bound and in which both systems are stable. A necessary and sufficient condition for such regions is derived using robust control theory. Notably, sufficiency can be expressed in terms of a linear matrix inequality. We demonstrate the approach by considering the temperature control of a large-scale building complex.

June 2023 IEEE Control Systems Letters Article

Towards Grassmannian Dimensionality Reduction in MPC

Roland Schurig, Andreas Himmel, Rolf Findeisen

PDF BibTeX DOI: 10.1109/LCSYS.2023.3291229

Abstract

Model predictive control presents remarkable potential for the optimal control of dynamic systems. However, the necessity for an online solution to an optimal control problem often renders it impractical for control systems with limited computational capabilities. To address this issue, specialized dimensionality reduction techniques designed for optimal control problems have been proposed. In this paper, we introduce a methodology for designing a low-dimensional subspace that provides an ideal representation for a predefined finite set of high-dimensional optimizers. By characterizing the subspace as an element of a specific Riemannian manifold, we leverage the unique geometric structure of the subspace. Subsequently, the optimal subspace is identified through optimization on the Riemannian manifold. The dimensionality reduction for the model predictive control scheme is achieved by confining the search space to the optimized low-dimensional subspace, enhancing both efficiency and applicability.

June 2023 IEEE Nanotechnology Magazine Article

Experimental Research in Synthetic Molecular Communications – Part I

Sebastian Lotter, Lukas Brand, Vahid Jamali, Maximilian Schäfer, Helene M. Loos, Harald Unterweger, Sandra Greiner, Jens Kirchner, Christoph Alexiou, Dietmar Drummer, Georg Fischer, Andrea Buettner, Robert Schober

BibTeX DOI: 10.1109/MNANO.2023.3262100

Abstract

Since its emergence from the communication engineering community around one and a half decades ago, the field of Synthetic Molecular Communication (SMC) has experienced continued growth, both in the number of technical contributions from a vibrant community and in terms of research funding. Throughout this process, the vision of SMC as a novel, revolutionary communication paradigm has constantly evolved, driven by feedback from theoretical and experimental studies, respectively. It is believed that especially the latter ones will be crucial for the transition of SMC towards a higher technology readiness level in the near future. In this spirit, we present here a comprehensive survey of experimental research in SMC. In particular, this survey focuses on highlighting the major drivers behind different lines of experimental research in terms of the respective envisioned applications. This approach allows us to categorize existing works and identify current research gaps that still hinder the development of practical SMC-based applications. Our survey consists of two parts: this paper and a companion paper. While the companion paper focuses on SMC with relatively long communication ranges, this paper covers SMC over short distances of typically not more than a few millimeters.

May 2023 17th European Conference on Antennas and Propagation (EuCAP 2023) Conference

Impact of Channel Models on Performance Characterization of RIS-Assisted Wireless Systems

Vahid Jamali, Walid Ghanem, Robert Schober, H. Vincent Poor

BibTeX DOI: 10.23919/EuCAP57121.2023.10133758

Abstract

The performance characterization of communication systems assisted by large reconfigurable intelligent surfaces (RISs) significantly depends on the adopted models for theunderlying channels. Under unrealistic channel models, the system performance may be over- or underestimated which yields inaccurate conclusions for the system design. In this paper, we review five channel models that are chosen to progressively improve the modeling accuracy for large RISs. For each channel model, we highlight the underlying assumptions, its advantages, and its limitations. We compare the system performance under the aforementioned channel models using RIS configuration algorithms from the literature and a new scalable algorithm proposed in this paper specifically for the configuration of extremely large RISs.

May 2023 Energies Article

Robust Placement and Control of Phase-Shifting Transformers Considering Redispatch Measures

Allan Santos, Florian Steinke

BibTeX DOI: 10.3390/en16114438

Abstract

Flexible AC transmission systems (FACTSs) can maximize capacity utilization under time-varying grid usage patterns by actively controlling the power flow of the transmission lines, e.g., with phase-shifting transformers (PST). In this paper, we propose an algorithm to determine the minimum number of PSTs and their location such that the grid can operate robustly for any realization of the (active) power set points from a known, continuous uncertainty set. As we show in our experiments, only considering a few extreme grid scenarios cannot provide this guarantee. The proposed algorithm considers the trade-offs between PST placement and operational decisions, such as PST control and redispatch. By minimizing the worst-case redispatch cost, it yields two affine linear control policies for these as a byproduct. Power flow is modeled as a constrained linear system, and the control design and actuator minimization tasks are formulated as a mixed-integer linear program (MILP). We also design a greedy algorithm, whose optimal value differs less than 20% from the MILP solution while being one to two orders of magnitude faster to compute. The proposed algorithm is evaluated for a small demonstrative 3-bus example and the IEEE 39 bus test system.

May 2023 22nd International Conference on Information Processing in Sensor Networks (IPSN’23) Conference

Hydra: Concurrent Coordination for Fault-tolerant Networking

Andreas Biri, Reto Da Forno, Tobias Kuonen, Fabian Mager, Marco Zimmerling, Lothar Thiele

BibTeX DOI: 10.1145/3583120.3587047

Abstract

Low-power wireless networks have the potential to enable applications that are of great importance to industry and society. However, existing network protocols do not meet the dependability requirements of many scenarios as the failure of a single node or link can completely disrupt communication and take significant time and energy to recover. This paper presents Hydra, a low-power wireless protocol that guarantees robust communication despite arbitrary node and link failures. Unlike most existing deterministic protocols, Hydra steers clear of centralized coordination to avoid a single point of failure. Instead, all nodes are equivalent in terms of protocol logic and configuration, performing coordination tasks such as synchronization and scheduling concurrently. This concept of concurrent coordination relies on a novel distributed consensus algorithm that yields provably unique decisions with low delay and energy overhead. In addition to a theoretical analysis, we evaluate Hydra in a multi-hop network of 23 nodes. Our experiments demonstrate that Hydra withstands random node failures without increasing coordination overhead and that it re-establishes efficient and reliable data exchange within seconds after a major disruption.

May 2023 22nd International Conference on Information Processing in Sensor Networks Conference

Demo Abstract: Building Battery-free Devices with Riotee

Kai Geissdörfer, Ingmar Splitt, Marco Zimmerling

PDF BibTeX DOI: 10.1145/3583120.3589808

Abstract

Battery-free devices eliminate the need for batteries, which are expensive, environmentally harmful, and require frequent replacement, thus reducing waste and making devices more cost-effective. We introduce Riotee, the next-generation platform for the battery-free Internet of Things. The platform comprises a base module, a debug probe that allows to conveniently update the firmware on the base module, and a number of expansion boards that extend the capabilities of the platform without the need to design a custom printed circuit board (PCB). We provide a brief overview of Riotee, and describe a demo setup that showcases the key functionality and how to get started with the platform in less than three minutes.

May 2023 2023 IEEE International Conference on Acoustics, Speech and Signal Processing Conference

Robust M-Estimation Based Distributed Expectation Maximization Algorithm with Robust Aggregation

Christian A. Schroth, Stefan Vlaski, Abdelhak M. Zoubir

BibTeX DOI: 10.1109/ICASSP49357.2023.10096191

May 2023 17th Conference of the European Chapter of the Association for Computational Linguistics Conference

Delving Deeper into Cross-lingual Visual Question Answering

Chen Liu, Jonas Pfeiffer, Anna Korhonen, Ivan Vulic, Iryna Gurevych

PDF BibTeX DOI: 10.18653/v1/2023.findings-eacl.186

Abstract

Visual question answering (VQA) is one of the crucial vision-and-language tasks. Yet, existing VQA research has mostly focused on the English language, due to a lack of suitable evaluation resources. Previous work on cross-lingual VQA has reported poor zero-shot transfer performance of current multilingual multimodal Transformers with large gaps to monolingual performance, without any deeper analysis. In this work, we delve deeper into the different aspects of cross-lingual VQA, aiming to understand the impact of 1) modeling methods and choices, including architecture, inductive bias, fine-tuning; 2) learning biases: including question types and modality biases in cross-lingual setups. The key results of our analysis are: 1. We show that simple modifications to the standard training setup can substantially reduce the transfer gap to monolingual English performance, yielding +10 accuracy points over existing methods. 2. We analyze cross-lingual VQA across different question types of varying complexity for different multilingual multimodal Transformers, and identify question types that are the most difficult to improve on. 3. We provide an analysis of modality biases present in training data and models, revealing why zero-shot performance gaps remain for certain question types and languages.

May 2023 44th Annual Conference of the European Assocoation for Computer Graphics (EUROGRAPHICS 2023) Conference

Quick-Pro-Build: A Web-based Approach for Quick Procedural 3D Reconstructions of Buildings.

B. Bohlender, M. Mühlhäuser, A. Sanchez Guinea

PDF BibTeX DOI: 10.2312/egs.20231001

Abstract

We present Quick-Pro-Build, a web-based approach for quick procedural 3D reconstruction of buildings. Our approach allows users to quickly and easily create realistic 3D models using two integrated reference views: street view and satellite view. We introduce a novel conditional and stochastic shape grammar to represent the procedural models based on the well-established CGA shape grammar. Based on our grammar and user interface, we propose 3 modalities for procedural modeling: 1) model from scratch, 2) copy, paste, and adapt, and 3) summarize, select and adapt. The third modality enables users to model a building by summarizing similar models into an architectural style description, selecting a model from the style description, and adapting it to the target building. Summarizing and selecting allows the third modality to be the most efficient option when modeling a building with a style similar to existing buildings. The third modality is enabled by a novel algorithm that can find and combine similarities from procedural models into a style description and allows learning the preference of the users for one model inside the style description.

April 2023 ACM 2023 CHI Conference on Human Factors in Computing Systems Conference

FIDO2 the Rescue? Platform vs. Roaming Authentication on Smartphones

Leon Würsching, Florentin Putz, Steffen Haesler, Matthias Hollick

PDF BibTeX DOI: 10.1145/3544548.3580993

Abstract

Modern smartphones support FIDO2 passwordless authentication using either external security keys or internal biometric authentication, but it is unclear whether users appreciate and accept these new forms of web authentication for their own accounts. We present the first lab study (N=87) comparing platform and roaming authentication on smartphones, determining the practical strengths and weaknesses of FIDO2 as perceived by users in a mobile scenario. Most participants were willing to adopt passwordless authentication during our in-person user study, but closer analysis shows that participants prioritize usability, security, and availability differently depending on the account type. We identify remaining adoption barriers that prevent FIDO2 from succeeding password authentication, such as missing support for contemporary usage patterns, including account delegation and usage on multiple clients.

April 2023 Computer Communications Article

Adaptive Global Coordination of Local Routing Policies for Communication Networks

Allan Santos, Amr Rizk, Florian Steinke

BibTeX DOI: 10.1016/j.comcom.2023.03.027

Abstract

We consider optimal routing of data packets in communication networks featuring time-variable flow rates and bandwidth limitations. Taking into account recent programmability developments in communication systems, we propose a two-level control scheme: routers with a programmable data plane implement local proportional control policies that forward the incoming data to different available output interfaces at line rate. The local controllers’ parameters are adapted periodically on a slower time scale by a logically centralized (software-defined) network controller running a global coordination algorithm that keeps the routing feasible and optimal with respect to a network metric, such as the average packet delay. A robust optimization approach is selected to handle traffic variations in-between global adaptation steps. The outcome is a non-convex Quadratically Constrained Quadratic Program (QCQP), for which we present an iterative solution approach that is computationally suitable for realistically-sized backbone communication networks. With simulation experiments, we demonstrate the advantages of adaptive, global routing coordination compared to fixed, globally or locally-determined policies, especially concerning packet loss.

April 2023 IEEE Nanotechnology Magazine Article

Experimental Research in Synthetic Molecular Communications - Part II

BibTeX DOI: 10.1109/MNANO.2023.3262377

Abstract

In this second part of our survey on experimental research in Synthetic Molecular Communication (SMC), we review works on long-range SMC systems, i.e., systems with communication ranges of more than a few millimeters. Despite the importance of experimental research for the evolution of SMC towards a mature communication paradigm that will eventually support revolutionary applications beyond the reach of today’s prevalent communication paradigms, the existing body of literature is still comparatively sparse. Long-range SMC systems have been proposed in the literature for information transmission in two types of fluid media, liquid and air. While both types of SMC systems, i.e., liquid-based and air-based systems, rely on encoding and transmitting information using molecules, they differ substantially in terms of the physical system designs and in the type of applications they are intended for. In this article, we present a systematic characterization of experimental works on long-range SMC that reveals the major drivers of these works in terms of the respective target applications. Furthermore, the physical designs for long-range SMC proposed in the literature are comprehensively reviewed. In this way, our survey will contribute to making experimental research in this field more accessible and identifying novel directions for future research.

April 2023 IEEE Transactions on Communications Article

Olfaction-Inspired MCs: Molecule Mixture Shift Keying and Cross-Reactive Receptor Arrays

Vahid Jamali, Helene M. Loos, Andreas Buettner, Robert Schober, H. Vincent Poor

BibTeX DOI: 10.1109/TCOMM.2023.3242379

Abstract

In this paper, we propose a novel concept for engineered molecular communication (MC) systems inspired by animal olfaction. We focus on a multi-user scenario where several transmitters wish to communicate with a central receiver. We assume that each transmitter employs a unique mixture of different types of signaling molecules to represent its message and the receiver is equipped with an array comprising R different types of receptors in order to detect the emitted molecule mixtures. The design of an MC system based on orthogonal molecule-receptor pairs implies that the hardware complexity of the receiver linearly scales with the number of signaling molecule types Q (i.e., R=Q ). Natural olfaction systems avoid such high complexity by employing arrays of cross-reactive receptors, where each type of molecule activates multiple types of receptors and each type of receptor is predominantly activated by multiple types of molecules albeit with different activation strengths. For instance, the human olfactory system is believed to discriminate several thousands of chemicals using only a few hundred receptor types, i.e., Q≫R . Motivated by this observation, we first develop an end-to-end MC channel model that accounts for the key properties of olfaction. Subsequently, we present the proposed transmitter and receiver designs. In particular, given a set of signaling molecules, we develop algorithms that allocate molecules to different transmitters and optimize the mixture alphabet for communication. Moreover, we formulate the molecule mixture recovery as a convex compressive sensing problem which can be efficiently solved via available numerical solvers. Finally, we present a comprehensive set of simulation results to evaluate the performance of the proposed MC designs revealing interesting insights regarding the design parameters. For instance, we show that mixtures comprising few types of molecules are best suited for communication since they can be more reliably detected by the cross-reactive array than one type of molecule or mixtures of many molecule types.

April 2023 International Journal of Human-Computer Studies Article

Preparedness nudging for warning apps? A mixed-method study investigating popularity and effects of preparedness alerts in warning apps

Jasmin Haunschild, Selina Pauli, Christian Reuter

BibTeX DOI: 10.1016/j.ijhcs.2023.102995

Abstract

Warning apps are used by many to receive warnings about imminent disasters. However, their potential for increasing awareness about general hazards and for increasing preparedness is currently underused. With a mixed-method design that includes a representative survey of the German population, a design workshop and an app evaluation experiment, this study investigates users’ preferences regarding non-acute preparedness alerts’ inclusion in crisis apps and the effectiveness of Nudging in this context. The experiment shows that while the social influence nudge had no significant effect compared to the control group without a nudging condition, the confrontational nudge increased the number of taken recommended preparedness measures. The evaluation indicates that the preparedness alerts increased users’ knowledge and their motivation to use a warning app. This motivation is, in contrast, decreased when the messages are perceived as a disruption. While many oppose push notifications, favor finding persuasively designed preparedness advice in a separate menu or as an optional notification.

April 2023 Journal of Systems and Software Article

The Uphill Journey of FaaS in the Open-Source Community

Nafise Eskandani, Guido Salvaneschi

PDF BibTeX DOI: 10.1016/j.jss.2022.111589

Abstract

Since its introduction in 2014 by Amazon, the Function as a Service (FaaS) model of serverless computing has set the expectation to fulfill the promise of on-demand, pay-as-you-go, infrastructure-independent processing, originally formulated by cloud computing. Yet, serverless applications are fundamentally different than traditional service-oriented software in that they pose specific performance (e.g., cold start), design (e.g., stateless), and development challenges (e.g., debugging). A growing number of cloud solutions have been continuously attempting to address each of these challenges as a result of the increasing popularity of FaaS. Yet, the characteristics of this model have been poorly understood; therefore, the challenges are poorly tackled. In this paper, we assess the state of FaaS in open-source community with a study on almost 2K real-world serverless applications. Our results show a jeopardized ecosystem, where, despite the hype of serverless solutions in the last years, a number of challenges remain untackled, especially concerning component reuse, support for software development, and flexibility among different platforms — resulting in arguably slow adoption of the FaaS model. We believe that addressing the issues discussed in this paper may help researchers shaping the next generation of cloud computing models.

March 2023 Other

Lessons Learned: Koordination im Katastrophenmanagement

Michèle Knodt, Eva Platzer

PDF BibTeX DOI: 10.5281/zenodo.7756274

Abstract

Das Starkregenereignis vom Sommer 2021 im Ahrtal hat die Debatte über die Bewältigung von Katastrophen und deren Folgen, wie u.a. langanhaltende Stromausfälle, ganz nach oben auf die Tagesordnung befördert. Zusammen mit den 2022 deutlich gewordenen Herausforderungen des Klimawandels und den möglichen Auswirkungen des Kriegs in der Ukraine wird verstärkt über das Verbesserungspotenzial des deutschen Bevölkerungs- und Katastrophenschutzes diskutiert. Dieses Policy Paper soll dabei einen Beitrag zur Debatte leisten. Es wird gezeigt, wie wichtig ein gut ausgebautes und organisiertes Katastrophenmanagement ist und wo aktuelle Schwachstellen liegen. Darüber hinaus werden Handlungsempfehlungen zur Verbesserung des Katastrophenmanagements gegeben. Dabei fokussieren wir uns vor allem auf Koordinationsprozesse zwischen den Beteiligten des Katastrophenmanagements in der direkten Reaktion auf das Ereignis und dessen Bewältigung. Wir werden in unserer Analyse aber auch die Konsequenzen unserer Analyse für die Vorbereitung auf zukünftige Katastrophen mit einbeziehen. Wir verstehen unsere „Lessons Learned“ in diesem Papier als Beitrag zur aktuellen Diskussion, der sich auch das kürzlich beschlossene gemeinsame Kompetenzzentrum Bevölkerungsschutz (GeKoB) annimmt. Das sich damit geöffnete „window of opportunity“ wollen wir nutzen, um Verbesserungspotenzial in der koordinierten Zusammenarbeit auf allen Ebenen im deutschen Bevölkerungs- und Katastrophenschutz aufzudecken. Unsere Handlungsempfehlungen für ein besseres Katastrophenmanagement lassen sich in vier Punkten zusammenfassen: Verbesserung der Koordination innerhalb und zwischen den Katastrophenschutz- und Verwaltungsstäben durch (a) gut vernetzte Expertenteams zur Unterstützung lokaler Katastrophenschutzstabsmitglieder, (b) Überarbeitung des Ausbildungs- und Einsatzkonzeptes der Verwaltungsstäbe und (c) Vereinheitlichung der (Fach-)Sprache im Einsatz Verbesserung der Koordination der Stäbe mit den Einsatzkräften: Transparenz und Routine fördern Verbesserung der Rolle der politisch Verantwortlichen: Ausbildung und Einbindung der politisch Verantwortlichen auf allen Ebenen Verbessertes Schnittstellenmanagement zwischen Stäben und Zivilgesellschaft: Spontanhelfer*innen als Ressource begreifen

March 2023 Transforming Cities Article

Krisenmanagement im Ahrtal 2021

Eva Platzer, Michèle Knodt

PDF BibTeX

Abstract

Die Überschwemmungen im Ahrtal im Sommer 2021 zeigten die Herausforderungen für den Katastrophenschutz bei der Koordination der Stäbe mit der Zivilbevölkerung, den Einsatzkräften und den politisch Verantwortlichen als zentrale Akteure für die Bewältigung eines Ereignisses. Der Beitrag zeigt die Notwendigkeit einer funktionierenden Koordination zwischen diesen Beteiligten. Auf Grundlage von Berichten und Experteninterviews werden Koordinationsprobleme identifiziert und Optionen formuliert: Verbesserte Koordination zwischen Stäben und Zivilgesellschaft sowie zwischen Stäben und Einsatzkräften und die Befähigung politisch Verantwortlicher zur Gesamtkoordination.

March 2023 Tag der Hydrologie 2023: Nachhaltiges Wassermanagement - Regionale und Globale Strategien Conference

Concept of a smart environmental monitoring and flood warning system

Mehdi Koopaeidar, Britta Schmalz

BibTeX

Abstract

In the course of river basin investigation initiated by cities and municipalities of the state of Hessen, there is a need for local and regional flood protection measures. For this reason, we have established a research project within the LOEWE center emergenCITY with the aim of developing an environmental monitoring and warning system for early assessment and warning of floods and low flow based on real-time measurement data with data fusion from various sources using artificial intelligence. The project includes three main stages. First is developing a model including hydrological and hydraulic processes to estimate water amounts spatially and temporally. Thereafter combine the model with the artificial intelligence methods to create a smart flood forecast system. And at last, creation of a smart communication system for transferring data and alarm levels to authorities, emergency services and citizens. The integrated assessment and warning system will be developed according to cooperative governance. The research project has started in July 2022 and currently is on its first stage. The Schwarzbach catchment (Nauheim gauge) in the state of Hessen has been chosen as the study area. Moreover, the catchment has been divided to 11 sub-catchments and a lumped hydrological model developed based on a digital elevation model with a resolution of 1 meter using the HEC-HMS program. Furthermore, precipitation data obtained the German weather service (DWD) and discharge data from the Hessian State Agency for Nature Conservation, Environment and Geology (HLNUG) with resolutions of 10 and 15 minutes, respectively, were used. Eventually, within the research, we are going to look into different aspects of innovative and sustainable measures for improving the resilient infrastructures of digital cities that can withstand crises and disasters related to weather extremes.

March 2023 IEEE Transactions on Molecular, Biological and Multi-Scale Communications Article

Chemical Reactions-Based Detection Mechanism for Molecular Communications

Trang Ngoc Cao, Vahid Jamali, Wayan Wicke, Nikola Zlatanov, Phee Lep Yeoh, Jamie Evans, Robert Schober

BibTeX DOI: 10.1109/TMBMC.2023.3244649

Abstract

In molecular communications, the direct detection of signaling molecules may be challenging due to a lack of suitable sensors and interference from co-existing substances in the environment. Motivated by research in molecular biology, we investigate an indirect detection mechanism using chemical reactions between the signaling molecules and a molecular probe to produce an easy-to-measure product at the receiver. We consider two implementations of the proposed detection mechanism, i.e., unrestricted probe movement and probes restricted to a volume around the receiver. In general, the resulting reaction-diffusion equations that describe the concentrations of the reactant and product molecules in the system are non-linear and coupled, and cannot be solved in closed form. To evaluate these molecule concentrations, we develop an efficient iterative algorithm by discretizing the time variable and solving for the space variables of the concentration equations in each time step. In the special case when the concentration of the unrestricted probes is high and not significantly changed by the chemical reaction, we obtain insightful closed-form solutions. Our results show that the concentrations of the product molecules and the signalling molecules have a similar characteristic over time, i.e., a single peak and a long tail. We highlight that by carefully choosing the molecular probe and optimizing the decision threshold, the BER can be improved significantly such that a direct detection system is outperformed. Moreover, when the molecular probes are kept in a small volume around the receiver, fewer resources are needed to achieve a lower BER and/or a higher data rate compared to the case of unrestricted molecular probes.

March 2023 Energy Policy Article

Power blackout: Citizens’ contribution to strengthen urban resilience

Michèle Knodt, Anna Stöckl, Florian Steinke, Martin Pietsch, Gerrit Hornung, Jan-Philipp Stroscher

PDF BibTeX DOI: 10.1016/j.enpol.2023.113433

Abstract

A long-lasting, large-scale power blackout has a huge impact on the infrastructure of public life, as well as on critical infrastructure including electricity and water supply. At the same time, it can be observed that the share of renewable energies, and thus the possibility of self-sufficiency, has increased enormously in recent years. This contribution focuses on the question to what extend citizens are willing to share their electricity resources in order to make their city more resilient. In reference to Ostrom’s concept of club or common goods, it can be shown if and how the private good of citizen’s electricity resources can be transformed into a club or even a common good. Drawing on survey data from the city of Darmstadt we investigated the willingness to share electricity and to participate in participatory formats to enhance urban resilience.

February 2023 IEEE Communications Letters Article

Optimization-Based Phase-Shift Codebook Design for Large IRSs

Walid R. Ghanem, Vahid Jamali, Malte Schellmann, Hanwen Cao, Joseph Eichinger, Robert Schober

BibTeX DOI: 10.1109/LCOMM.2022.3225585

Abstract

In this letter, we focus on large intelligent reflecting surfaces (IRSs) and propose a new codebook construction method to obtain a set of pre-designed phase-shift configurations for the IRS unit cells. Since the complexity of online optimization and the overhead for channel estimation scale with the size of the phase-shift codebook, the design of small codebooks is of high importance. We consider both continuous and discrete phase-shift designs and formulate the codebook construction as optimization problems. To solve the optimization problems, we propose an optimal algorithm for the discrete phase-shift design and a locally optimal solution for the continuous design. Simulation results show that the proposed algorithms facilitate the construction of codebooks of different sizes and with different beamwidths. Moreover, the performance of the discrete phase-shift design with 2-bit quantization is shown to approach that of the continuous phase-shift design. Finally, our simulation results show that the proposed designs enable large transmit power savings compared to the existing linear and quadratic codebook designs.

February 2023 Water Article

Modeling and Validation of Residential Water Demand in Agent-Based Models: A Systematic Literature Review

Bernhard Jonathan Sattler, John Friesen, Andrea Tundis, Peter F. Pelz

PDF BibTeX DOI: 10.3390/w15030579

Abstract

Current challenges, such as climate change or military conflicts, show the great importance of urban supply infrastructures. In this context, an open question is how different scenarios and crises can be studied in silico to assess the interaction between the needs of social systems and technical infrastructures. Agent-based modeling is a suitable method for this purpose. This review investigates (i) how agent-based models of residential water demand should be validated, (ii) how such models are commonly built and (iii) validated, and (iv) how these validation practices compare to the recommendations drawn from question (i). Therefore, a systematic literature review using the PRISMA framework is conducted. Out of 207 screened papers, 35 models are identified with an emphasis on highly realistic models (i.e., highly detailed and representing specific real-world systems) for planning, management, and policy of urban water resources. While some models are thoroughly validated, quantified validation distinct from calibration data should be emphasized and used to communicate the confidence in results and recommendations drawn from the models. Pattern-oriented validation, validation on multiple levels and on higher moments of aggregated statistics should be considered more often. These findings expand prior literature by providing a more extensive sample of reviewed articles and recommending specific approaches for the validation of models.

January 2023 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Conference

Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time Series

Thomas Kreutz, Max Mühlhäuser, Alejandro Sanchez Guinea

BibTeX DOI: 10.1109/WACV56688.2023.00169

Abstract

In this work, we address the problem of unsupervised moving object segmentation (MOS) in 4D LiDAR data recorded from a stationary sensor, where no ground truth annotations are involved. Deep learning-based state-of- the-art methods for LiDAR MOS strongly depend on anno- tated ground truth data, which is expensive to obtain and scarce in existence. To close this gap in the stationary set- ting, we propose a novel 4D LiDAR representation based on multivariate time series that relaxes the problem of un- supervised MOS to a time series clustering problem. More specifically, we propose modeling the change in occupancy of a voxel by a multivariate occupancy time series (MOTS), which captures spatio-temporal occupancy changes on the voxel level and its surrounding neighborhood. To perform unsupervised MOS, we train a neural network in a self- supervised manner to encode MOTS into voxel-level feature representations, which can be partitioned by a clustering al- gorithm into moving or stationary. Experiments on station- ary scenes from the Raw KITTI dataset show that our fully unsupervised approach achieves performance that is com- parable to that of supervised state-of-the-art approaches.

January 2023 Elsevier Article

Power blackout: Citizens’ contribution to strengthen local resilience

Martin Pietsch, Michèle Knodt, Gerrit Hornung, Jan-Philipp Stroscher, Florian Steinke, Anna Stöckl

PDF BibTeX DOI: 10.1016/j.enpol.2023.113433

Abstract

January 2023 IEEE Transactions on Network Science and Engineering Article

Spatial-Temporal Modeling and Analysis of Reliability and Delay in Urban V2X Networks

Junliang Ye, Lin Xiang, Xiaohu Ge

PDF BibTeX DOI: 10.1109/TNSE.2023.3234284

Abstract

The fifth-generation (5 G) based vehicle-to-everything (V2X) communication is a promising technology to enhance both manned and unmanned driving systems. To this end, the 5 G V2X networks should satisfy stringent requirements on transmission reliability and delay for exchanging safety-critical messages (SCMs). A joint analysis of transmission reliability and delay in V2X communication networks is thus crucial, particularly in urban 5 G V2X networks. This was considered prohibitive due to the complicated spatial-temporal dynamics of V2X communications caused by interference, channel fading, as well as queueing and retransmission of SCMs. Moreover, urban 5 G V2X networks are typically deployed in a finite area, where locations of nodes are spatially correlated and cannot be conveniently modeled as Poisson point process (PPP). In this paper, we propose a novel binomial point process (BPP) based analytic framework for modeling the spatial-temporal dynamics of urban 5 G V2X communications and characterizing transmission reliability and delay of SCM exchange jointly. The presented framework captures not only the spatial distribution of interference and channel fading during uplink and downlink transmissions, but also the temporal dynamics associated with queueing and retransmissions of SCMs. Exploiting the stochastic geometry theory and queueing theory, closed-form expressions of transmission reliability and delay are derived, which are further validated using Monte Carlo simulations. Both the numerical and simulation results reveal complicated couplings between the transmission reliability and delay in different operation regimes. Nevertheless, the proposed analytical framework can accurately capture the reliability-delay relations.

2023 International Conference on Modelling and Applied Simulation (MAS 2023) Conference

A framework for the simulation-based selection of social models for socio-technical models of infrastructures using technical requirements analysis

Bernhard Jonathan Sattler, Jannik Stadler, Andrea Tundis, John Friesen, Peter F. Pelz

PDF BibTeX DOI: 10.46354/i3m.2023.mas.010

Abstract

Urbanization increases the importance of urban infrastructures, with computer models and simulation being important tools for their planning and management. Human factors are increasingly included into infrastructure models, creating socio-technical models. This paper proposes a novel framework for selecting these social (sub-)models. For this, requirements analysis of the technical system is used to identify critical physical parameters. The impact of different assumptions in the social model on the critical physical parameters are determined using simulation and hypothesis testing. This impact is used to determine the relevance of the differing assumptions and to select the right social model. Finally, a preliminary case study of the water distribution system of Darmstadt, Germany, is used to show the efficacy of the framework by comparing two water demand models. The results of the case study show, that the framework can be used to quantify the relevant system behavior and test the significance of model assumptions.

2023 INFORMATIK 2023 - 53. Jahrestagung der Gesellschaft für Informatik e.V. Conference

Das Netz hat Geschichte

Jonas Franken, Marco Zivkovic, Nadja Thiessen, Jens Ivo Engels, Christian Reuter

BibTeX DOI: 10.18420/inf2023_159

Abstract

Kritische Infrastrukturen sind häufig über Jahrzehnte gewachsene, komplexe Netze. Den- noch fehlt derzeit die historische Perspektive auf die Aufschichtungstendenzen von Technologien in den Sektoren, die für die Gesellschaft essenzielle Dienste bereitstellen. Ein besseres Verständnis von Ausbreitungs-, Ausbau-, Ersatz- und Ausmusterungsprozessen kann Entscheidungshilfe und Orientierung für resilientere Versorgungsnetzarchitekturen in der Zukunft geben. Kompatibilitäts- probleme mit Legacy-Soft- und Hardware sind bekannte Phänomene in vielen KRITIS-Einrichtun- gen. Entsprechend gewinnen Wissens- und Erfahrungstransfers bei zunehmend komplexen, dennoch über Jahrzehnte verwendete Technologien in landwirtschaftlichen Betrieben enorm an Bedeutung. Der Beitrag vollzieht die Konzeption und Fragestellungen eines interdisziplinären Forschungspro- jekts nach, in welchem die Verwundbarkeit der kritischen Infrastruktursektoren Verkehr und Kom- munikation im Rhein-Main-Gebiet analysiert wird. Von den Leistungen beider Sektoren hängt die digitale Landwirtschaft stark ab. Insbesondere rurale, beim digitalen und Schienennetzausbau häufig vernachlässigte Gebiete werden dabei mittels explorativer Interviewstudie und anschließender ar- chivbasierter, quantitativer Überprüfung der zuvor generierten Hypothesen aus einer raum-zeitli- chen und technischen Perspektive untersucht.

2023 Designing Resilience Global (DRG) International Symposium and Competition Conference

Designing Resilience Global (DRG) - International Symposium and Competition - Documentation

PDF BibTeX DOI: 10.26083/tuprints-00023774

Abstract

The 2022 Designing Resilience Global (DRG) Symposium and Competition took part from June 20th to June 24th. The documentation includes transcripts of all keynote speeches of renown experts in academia and practice and the submissions of all universities that took part in the international competition.

2023 26th International Conference on Artificial Intelligence and Statistics Conference

Learning Sparse Graphon Mean Field Games

Christian Fabian, Kai Cui, Heinz Koeppl

PDF BibTeX

Abstract

Although the field of multi-agent reinforcement learning (MARL) has made considerable progress in the last years, solving systems with a large number of agents remains a hard challenge. Graphon mean field games (GMFGs) enable the scalable analysis of MARL problems that are otherwise intractable. By the mathematical structure of graphons, this approach is limited to dense graphs which are insufficient to describe many real-world networks such as power law graphs. Our paper introduces a novel formulation of GMFGs, called LPGMFGs, which leverages the graph theoretical concept of Lp graphons and provides a machine learning tool to efficiently and accurately approximate solutions for sparse network problems. This especially includes power law networks which are empirically observed in various application areas and cannot be captured by standard graphons. We derive theoretical existence and convergence guarantees and give empirical examples that demonstrate the accuracy of our learning approach for systems with many agents. Furthermore, we extend the Online Mirror Descent (OMD) learning algorithm to our setup to accelerate learning speed, empirically show its capabilities, and conduct a theoretical analysis using the novel concept of smoothed step graphons. In general, we provide a scalable, mathematically well-founded machine learning approach to a large class of otherwise intractable problems of great relevance in numerous research fields.

2023 Der Gemeinderat Article

Infrastruktursysteme: Wenn alles verbunden und verwoben ist

Jens Ivo Engels

BibTeX

2023 2023 2nd International Conference on Intelligent Systems Design and Engineering Applications (ISDEA 2023) Conference

Persistent UAV formation flight by dynamic agent replacement and leader selection

Maximilian Bauer, Yasin Alhamwy, Kurt Geihs

BibTeX DOI: 10.1007/978-981-99-6303-4_9

2023 IEEE Wireless Communications and Networking Conference (WCNC) Conference

SUN: a simulated UAV network testbed with hardware-in-the-loop SDR support

Lars Baumgärtner, Maximilian Bauer, Bastian Bloessl

BibTeX DOI: 10.1109/wcnc55385.2023.10119014

2023 IEEE Control Systems Letters Article

Mean Field Games on Weighted and Directed Graphs via Colored Digraphons

C. Fabian, K. Cui, H. Koeppl

PDF BibTeX DOI: 10.1109/LCSYS.2022.3227453

2023 Environment Systems and Decisions Article

Resilience beyond insurance: coordination in crisis governance

Eva Katharina Platzer, Michèle Knodt

PDF BibTeX DOI: 10.1007/s10669-023-09938-7

Abstract

The latest report by the Intergovernmental Panel on Climate Change (IPCC) warns of an increase in heavy rainfall events due to global warming and climate change, which can result in significant economic costs for insurance companies and businesses. To address this challenge, insurance companies are focusing on developing new risk management strategies and offering new products such as flood insurance. However, the article argues that effective and feasible coordination shortens recovery time and can therefore drastically reduce the financial costs of a crisis—that is, the insurance costs. The paper analyses the deficit in crisis management during heavy rain events in Germany, based on the 2021 Ahr valley flood. The analysis is conducted based on document analysis and interviews and focuses on three areas of deficit: coordination between crisis staffs and (1) civil society, (2) emergency responders, and (3) political leaders. The paper highlights the importance of coordination during a crisis, which can help to address the crisis more efficiently and effectively, minimise damage and get communities back on their feet faster. The paper recommends policy changes to improve interface management and disaster management coordination.

2023 ZD-Aktuell Article

EU-Kommission: Verordnungsentwurf für ein interoperables Europa

Jan-Philipp Muttach, Rosa Jacobi

BibTeX

2023 integration Article

Energiesicherheit-Nachhaltigkeit-Nexus in Zeiten des Krieges: Krisengesteuerte Notfallmaßnahmen für den Green Deal?

Michèle Knodt, Marc Ringel

PDF BibTeX DOI: 10.5771/0720-5120-2023-3-282

2023 Mobility Design: Die Zukunft der Mobilität gestalten Band 2: Forschung Other

Mobilität als Schlüssel zur lebenswerten Stadt

Björn Hekmati, Annette Rudolph-Cleff

PDF BibTeX DOI: 10.1515/9783868597936

Abstract

Klimawandel und Ressourcenverknappung, aber auch der stetig steigende Verkehrsaufwand machen es unabdingbar, neue Lösungen für eine umweltschonende und menschenfreundliche Mobilität zu entwickeln. Mit dem Ausbau digitaler Informationssysteme werden wir zukünftig unterschiedliche Verkehrsträger entsprechend unseren Bedürfnissen leicht kombinieren können. Diese Entwicklungen sind für die Gestaltung verschiedener Mobilitätsräume eine große Herausforderung. Lag der Schwerpunkt in Band 1 auf der Praxis, versammelt Band 2 nun Forschungen aus den Bereichen Design, Architektur, Stadtplanung, Geografie, Sozialwissenschaft, Verkehrsplanung, Psychologie und Kommunikationstechnologie. Die aktuelle Diskussion über die Verkehrswende wird um die Perspektive des nutzer*innenzentrierten Mobilitätsdesigns erweitert.

2023 Technische Universität Darmstadt Darmstadt Thesis

Decentralized Ultra-Reliable Low-Latency Communications through Concurrent Cooperative Transmission

Robin Klose

PDF BibTeX DOI: 10.26083/tuprints-00024070

Abstract

Emerging cyber-physical systems demand for communication technologies that enable seamless interactions between humans and physical objects in a shared environment. This thesis proposes decentralized URLLC (dURLLC) as a new communication paradigm that allows the nodes in a wireless multi-hop network (WMN) to disseminate data quickly, reliably and without using a centralized infrastructure. To enable the dURLLC paradigm, this thesis explores the practical feasibility of concurrent cooperative transmission (CCT) with orthogonal frequency-division multiplexing (OFDM). CCT allows for an efficient utilization of the medium by leveraging interference instead of trying to avoid collisions. CCT-based network flooding disseminates data in a WMN through a reception-triggered low-level medium access control (MAC). OFDM provides high data rates by using a large bandwidth, resulting in a short transmission duration for a given amount of data. This thesis explores CCT-based network flooding with the OFDM-based IEEE 802.11 Non-HT and HT physical layers (PHYs) to enable interactions with commercial devices. An analysis of CCT with the IEEE 802.11 Non-HT PHY investigates the combined effects of the phase offset (PO), the carrier frequency offset (CFO) and the time offset (TO) between concurrent transmitters, as well as the elapsed time. The analytical results of the decodability of a CCT are validated in simulations and in testbed experiments with Wireless Open Access Research Platform (WARP) v3 software-defined radios (SDRs). CCT with coherent interference (CI) is the primary approach of this thesis. Two prototypes for CCT with CI are presented that feature mechanisms for precise synchronization in time and frequency. One prototype is based on the WARP v3 and its IEEE 802.11 reference design, whereas the other prototype is created through firmware modifications of the Asus RT-AC86U wireless router. Both prototypes are employed in testbed experiments in which two groups of nodes generate successive CCTs in a ping-pong fashion to emulate flooding processes with a very large number of hops. The nodes stay synchronized in experiments with 10 000 successive CCTs for various modulation and coding scheme (MCS) indices and MAC service data unit (MSDU) sizes. The URLLC requirement of delivering a 32-byte MSDU with a reliability of 99.999 % and with a latency of 1 ms is assessed in experiments with 1 000 000 CCTs, while the reliability is approximated by means of the frame reception rate (FRR). An FRR of at least 99.999 % is achieved at PHY data rates of up to 48 Mbit/s under line-of-sight (LOS) conditions and at PHY data rates of up to 12 Mbit/s under non-line-of-sight (NLOS) conditions on a 20 MHz wide channel, while the latency per hop is 48.2 µs and 80.2 µs, respectively. With four multiple input multiple output (MIMO) spatial streams on a 40 MHz wide channel, a LOS receiver achieves an FRR of 99.5 % at a PHY data rate of 324 Mbit/s. For CCT with incoherent interference, this thesis proposes equalization with time-variant zero-forcing (TVZF) and presents a TVZF receiver for the IEEE 802.11 Non-HT PHY, achieving an FRR of up to 92 % for CCTs from three unsyntonized commercial devices. As CCT-based network flooding allows for an implicit time synchronization of all nodes, a reception-triggered low-level MAC and a reservation-based high-level MAC may in combination support various applications and scenarios under the dURLLC paradigm.

2023 Universitäts- und Landesbibliothek Darmstadt Darmstadt Other

Urbane Datenplattformen und Resilienz der Städte: Status quo in Deutschland und Empfehlungen für kommunale Akteure

Michaela Leštáková, Lucía Wright-Contreras, Leonie Schiermeyer

PDF BibTeX DOI: 10.26083/tuprints-00024392

Abstract

Digitalisierung in Städten ist ein globaler Trend. Auch in der Bundesrepublik Deutschland streben viele Städte an, sogenannte Smart Cities zu werden. Um dieses Ziel zu erreichen, werden in Städten zunehmend Daten zu verschiedenen Themenbereichen gesammelt – mit der Absicht, den Informationsaustausch zwischen Behörden, Bürgerinnen und Bürgern und anderen städtischen Akteuren einfacher, effizienter und transparenter zu gestalten. Zu diesem Zweck werden häufig sogenannte urbane Datenplattformen eingesetzt. In diesem Praxisdossier wird die Rolle von urbanen Datenplattformen im Kontext von Resilienz und Smart Cities untersucht. In Kooperation zwischen dem LOEWE-Zentrum emergenCITY und Haselhorst Associates wurden zu diesem Zweck zwei Forschungsfragen definiert: 1) Können urbane Datenplattformen dazu beitragen, die Resilienz der Stadt zu erhöhen? (Resilienz durch IKT) und 2) Sind die existierenden urbanen Datenplattformen in sich resilient? (Resilienz für IKT). Zur Beantwortung dieser Fragen wurden über 400 deutsche Städte hinsichtlich des Vorhandenseins oder der geplanten Einführung einer städtischen Datenplattform untersucht. Ergänzt wurden die Ergebnisse durch eine Analyse der Smart-Cities-Modellprojekte. Basierend auf der Analyse werden 6 Empfehlungen für kommunale Akteure formuliert.

2023 Jahrbuch des Föderalismus 2023 Other

Mangelnde Koordination beim Hochwasserschutz im deutschen Mehrebenensystem. Das Beispiel der Hochwasserkatastrophe im Ahrtal im Sommer 2021

Eva Platzer, Michèle Knodt

BibTeX DOI: 10.5771/9783748918776

2023 Computer und Recht Article

Verarbeitung personenbezogener Daten in KI-Reallaboren nach dem KI-VO-E – Eine Herausforderung für den datenschutzrechtlichen Zweckbindungsgrundsatz

Jan-Philipp Muttach, Hendrik Link

BibTeX

2023 Technische Universität Darmstadt Wiesbaden Thesis

Schutz Kritischer Infrastrukturen in Deutschland: Implementation zum Infrastrukturschutz an Verkehrsflughäfen

Eva Platzer

BibTeX DOI: 10.1007/978-3-658-43053-5

2023 51st International Conference on Parallel Processing Conference

Learning Mean-Field Control for Delayed Information Load Balancing in Large Queuing Systems

A. Tahir, K. Cui, H. Koeppl

PDF BibTeX DOI: 10.1145/3545008.3545025

2023 Serious Games Conference

Knowledge Transfer in Disaster Risk Management (DRM): Design of a Serious Game

Joachim Schulze, Tom Sauer, Annette Rudolph-Cleff, Michèle Knodt

BibTeX DOI: 10.1007/978-3-031-44751-8_35

2023 WiSec ‘23: 16th ACM Conference on Security and Privacy in Wireless and Mobile Networks Conference

DEMO: Secure Bootstrapping of Smart Speakers Using Acoustic Communication

Markus Scheck, Florentin Putz, Frank Hessel, Hermann Leinweber, Jonatan Crystall, Matthias Hollick

PDF BibTeX DOI: 10.26083/tuprints-00024180

Abstract

Smart speakers are highly privacy-sensitive devices: They are located in our homes and provide an Internet-enabled microphone, making them a prime target for attackers. The pairing between a client device and the speaker must be protected to prohibit adversaries from accessing the device. Most commercial protocols are vulnerable to nearby adversaries as they do not probe for human presence at the speaker or proximity between both devices. In addition to security, the protocol must provide a user-friendly way for initial bootstrapping of the speaker. We design an open pairing protocol for the establishment of a shared secret between both devices using acoustic messaging to guarantee proximity, and release our implementation for the smart speaker as well as Android and Linux clients as open-source software on GitHub.

2023 IEEE Global Humanitarian Technology Conference (GHTC) Conference

BPoL: a disruption-tolerant LoRa network for disaster communication

Daniel Schmidt, Franz Kuntke, Maximilian Bauer, Lars Baumgärtner

BibTeX DOI: 10.1109/ghtc56179.2023.10354717

2023 Sensors Article

Energy-Efficient Decentralized Broadcasting in Wireless Multi-Hop Networks

Artur Sterz, Robin Klose, Markus Sommer, Jonas Höchst, Jakob Link, Bernd Simon, Anja Klein, Matthias Hollick, Bernd Freisleben

BibTeX DOI: 10.3390/s23177419

Abstract

Several areas of wireless networking, such as wireless sensor networks or the Internet of Things, require application data to be distributed to multiple receivers in an area beyond the transmission range of a single node. This can be achieved by using the wireless medium’s broadcast property when retransmitting data. Due to the energy constraints of typical wireless devices, a broadcasting scheme that consumes as little energy as possible is highly desirable. In this article, we present a novel multi-hop data dissemination protocol called BTP. It uses a game-theoretical model to construct a spanning tree in a decentralized manner to minimize the total energy consumption of a network by minimizing the transmission power of each node. Although BTP is based on a game-theoretical model, it neither requires information exchange between distant nodes nor time synchronization during its operation, and it inhibits graph cycles effectively. The protocol is evaluated in Matlab and NS-3 simulations and through real-world implementation on a testbed of 75 Raspberry Pis. The evaluation conducted shows that our proposed protocol can achieve a total energy reduction of up to 90% compared to a simple broadcast protocol in real-world experiments.

2023 Technische Universität Darmstadt Darmstadt Thesis

Aerial Network Assistance Systems for Post-Disaster Scenarios : Topology Monitoring and Communication Support in Infrastructure-Independent Networks

Julian Zobel

PDF BibTeX DOI: 10.26083/tuprints-00023043

Abstract

Communication anytime and anywhere is necessary for our modern society to function. However, the critical network infrastructure quickly fails in the face of a disaster and leaves the affected population without means of communication. This lack can be overcome by smartphone-based emergency communication systems, based on infrastructure-independent networks like Delay-Tolerant Networks (DTNs). DTNs, however, suffer from short device-to-device link distances and, thus, require multi-hop routing or data ferries between disjunct parts of the network. In disaster scenarios, this fragmentation is particularly severe because of the highly clustered human mobility behavior. Nevertheless, aerial communication support systems can connect local network clusters by utilizing Unmanned Aerial Vehicles (UAVs) as data ferries. To facilitate situation-aware and adaptive communication support, knowledge of the network topology, the identification of missing communication links, and the constant reassessment of dynamic disasters are required. These requirements are usually neglected, despite existing approaches to aerial monitoring systems capable of detecting devices and networks. In this dissertation, we, therefore, facilitate the coexistence of aerial topology monitoring and communications support mechanisms in an autonomous Aerial Network Assistance System for infrastructure-independent networks as our first contribution. To enable system adaptations to unknown and dynamic disaster situations, our second contribution addresses the collection, processing, and utilization of topology information. For one thing, we introduce cooperative monitoring approaches to include the DTN in the monitoring process. Furthermore, we apply novel approaches for data aggregation and network cluster estimation to facilitate the continuous assessment of topology information and an appropriate system adaptation. Based on this, we introduce an adaptive topology-aware routing approach to reroute UAVs and increase the coverage of disconnected nodes outside clusters. We generalize our contributions by integrating them into a simulation framework, creating an evaluation platform for autonomous aerial systems as our third contribution. We further increase the expressiveness of our aerial system evaluation, by adding movement models for multicopter aircraft combined with power consumption models based on real-world measurements. Additionally, we improve the disaster simulation by generalizing civilian disaster mobility based on a real-world field test. With a prototypical system implementation, we extensively evaluate our contributions and show the significant benefits of cooperative monitoring and topology-aware routing, respectively. We highlight the importance of continuous and integrated topology monitoring for aerial communications support and demonstrate its necessity for an adaptive and long-term disaster deployment. In conclusion, the contributions of this dissertation enable the usage of autonomous Aerial Network Assistance Systems and their adaptability in dynamic disaster scenarios.

2023 Technische Universität Darmstadt Berlin Thesis

Gefährdung städtischer Infrastruktur durch Hochwasser: Wahrnehmungen und Bewältigungsstrategien in Mannheim und Dresden 1918-1989

Nadja Thiessen

BibTeX DOI: 10.1515/9783110734676

Abstract

Mannheim und Dresden sind seit ihrer Gründung durch die Dynamiken des Wassers geprägt. Vor allem die zyklisch auftretenden Hochwasser führen zu Gefährdungen. Im kurzen 20. Jahrhundert existierte ein konstanter Umgang damit innerhalb der Städte. Die Studie fokussiert diese Bewältigungsstrategien, die unter dem Einfluss lokalspezifischer Faktoren wie der Stadtentwicklung sowie übergeordneten politischen und wirtschaftlichen Bedingungen standen.

2023 European Societies Article

Civil society and sense of community in Ukraine: from dormancy to action

Kateryna Zarembo, Eric Martin

BibTeX DOI: 10.1080/14616696.2023.2185652

Abstract

The academic literature offers different views on the strength of Ukraine’s civil society, but Ukraine’s massive civic engagement and collective action, most recently in defense against Russian aggression, offers a startling picture of grass-root activism. Based on interviews, surveys and archival research, we highlight changes and nuances to Ukrainian civil society, civic engagement and motivations over time, from Euromaidan, through the hybrid Russian aggression in the East, to the recent full-scale Russian invasion. In doing so, we explore a more inclusive understanding of civil society complemented by sense of community and community responsibility.

December 2022 Engineering Proceedings Article

Analysis of Helicopter Flights in Urban Environments for UAV Traffic Management

David Hünemohr, Maximilian Bauer, Jan Kleikemper, Markus Peukert

PDF BibTeX DOI: 10.3390/engproc2022028010

Abstract

Future air mobility will consist of increased unmanned aerial vehicle (UAV) traffic operating in urban areas. Currently, the lower airspace in these environments is mainly used by traffic operating under visual flight rules, particularly helicopters in emergency medical services (HEMS). In the presented work, we analyze urban HEMS missions with automatic dependent surveillance-broadcast (ADS-B) data to identify the potential benefits to support UAV traffic management (UTM). In our methodology, we first restrict an existing HEMS ADS-B data set to a specific city and then further process it to extract the valid HEMS flights. Because no other mission information is available, we apply rule-based algorithms to define different helicopter flight segments and characterize specific HEMS mission segments. The resulting data set is analyzed to extract the characteristic information about the HEMS traffic within the city. The methodology is applied to the ADS-B HEMS flight data in the area of Berlin. The results show that the HEMS and flight segments can be identified robustly, and specific flight patterns are characteristic for them. Based on the results of this analysis, UAV traffic alert strategies are proposed to demonstrate the potential benefit of integrating ADS-B data statistics for UTM.

December 2022 33rd International Symposium on Personal, Indoor and Mobile Radio Communications Conference

Connectivity Analysis for Large-Scale Intelligent Reflecting Surface Aided mmWave Cellular Networks

Yi Wang, Lin Xiang, Jing Zhang, Xiaohu Ge

BibTeX DOI: 10.1109/PIMRC54779.2022.9977979

Abstract

This paper presents a stochastic geometry framework for modeling and evaluating the connectivity of uplink transmission in a large-scale intelligent reflecting surface (IRS) assisted millimeter-wave (mmWave) communication network, where the uplink user equipments (UEs) attempt to communicate with the nearest base stations (BSs) either without or with the help of an IRS. We propose a novel elliptical geometry model, which can effectively capture the impact of IRS location and orientation, as well as incident/reflection angle on mmWave signal propagation, while, at the same time, significantly simplifying the analysis of the system performance. Employing the elliptical geometry model, the approximate reflection probability of IRS as well as its upper and lower bounds are derived in closed form. Based on these results, we further analyze the successful connection probability of uplink UEs for IRS-assisted mmWave cellular networks. Our results show that compared with conventional direct UE-to-BS communication without IRS, indirect communication with the aid of IRS exhibits a slower decaying in the connection probability as the communication distance increases, as the latter can significantly increase the connection probability for cell-edge UEs. Moreover, for mmWave BSs with small receiving power thresholds, the deployment of IRS can effectively mitigate the impact of blockages to improve mmWave signal propagation.

December 2022 33rd International Symposium on Personal, Indoor and Mobile Radio Communications Conference

UAV-Assisted Delay-Sensitive Communications with Uncertain User Locations: A Cost Minimization Approach

Burak Yilmaz, Lin Xiang, Anja Klein

BibTeX DOI: 10.1109/PIMRC54779.2022.9977912

Abstract

In this paper, we consider optimal resource allocation for unmanned aerial vehicle (UAV)-assisted delay-sensitive communications, where a UAV flies to deliver time-critical messages to multiple ground users (GUs) as soon as possible. However, the GUs’ locations cannot be perfectly known at the UAV, which may jeopardize the timeliness of message delivery to the GUs. To tackle this challenge, we consider a disk-based fixed-rate transmission scheme at the UAV, which can exploit the mobility of the UAV to facilitate timely communications despite uncertain user locations. Consequently, the system performance hinges on the UAV’s flight trajectory and the scheduling of GUs, which are further optimized using a cost minimization approach. Thereby, a general class of delay-aware cost functions, referred to as the cost of delivery delay (CoDD), is defined taking into account the diverse delay-sensitivity requirements of the GUs, and we jointly optimize the user scheduling and the UAV’s trajectory for minimization of the sum CoDD of all GUs incurred before the UAV’s mission completes. The formulated optimization problem is a nonconvex mixed-integer nonlinear program. Exploiting the underlying structure of this problem, we further propose two novel low-complexity solutions based on approximate dynamic programming (DP). Simulation results show that the proposed schemes can flexibly adjust the UAV’s flight trajectory and resource allocation according to the GUs’ individual delivery delays, delay tolerance, and location uncertainty, which translates into significantly lower sum CoDD for the GUs than several benchmark schemes.

December 2022 2022 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR) Conference

Visualization of Machine Learning Uncertainty in AR-Based See-Through Applications

Achref Doula, Lennart Schmidt, Max Mühlhäuser, Alejandro Sanchez Guinea

BibTeX DOI: 10.1109/AIVR56993.2022.00022

Abstract

Augmented reality see-through applications rely mostly on machine learning models to detect and localize occluded objects. In this case, the user is usually presented the result with the highest probability without taking into account the uncertainty of the model. However, the uncertainty plays a vital role when considering applications where a critical decision-making process relies heavily on the predictions of the model, such as in the case where occluded cars are shown to a driver. In this work, we conduct an investigation of the effects of communicating the uncertainty of machine learning models to users in AR-based see-through applications. Through a controlled user study, we compare three visualization modes: no visualization, most probable output, and probability distribution. The results of our evaluation reveal that when considering the visualizations, each of them lead to comparable results in terms of speed and accuracy of the decision-making process. A relevant finding is that participants considered uncertainty as a substantial part of the output of machine learning models and needs to be delivered with the results. An additional important conclusion is that the preference of users over a specific visualization is strongly dependent on the particular use case.

December 2022 GLOBECOM 2022 - 2022 IEEE Global Communications Conference Conference

RIS-assisted beamforming for energy efficiency in multiuser downlink transmissions

Jaime Quispe, Tarcisio Ferreira Maciel, Yuri Carvalho Barbosa Silva, Anja Klein

BibTeX DOI: 10.1109/GCWkshps56602.2022.10008573

Abstract

Reconfigurable intelligent surface (RIS) based reflections is a promising approach to increase spectral efficiency (SE) and reduce power consumption of wireless communications systems. This paper investigates the trade-off between these two metrics by considering the energy efficiency (EE) maximization of an RIS-assisted multiuser downlink transmission from a multiantenna base station (BS) to multiple single-antenna users while satisfying constraints on quality-of-service (QoS), RIS phase shifts, and BS maximum transmit power. We consider a coordinated beamforming scheme and propose a joint optimization procedure based on the Dinkelbach, fractional programming, and semi-definite relaxation (SDR) methods. Simulation results show that the RIS-assisted system is more energy-efficient than its counterpart without RIS and that the RIS, particularly when it is equipped with a large number of antenna elements, can simultaneously improve the SE and power consumption of the transmission. Furthermore, the presented algorithm achieves good-quality solutions that are competitive to the obtained via exhaustive search with branch-reduce-and-bound (BRnB) methods and requires fewer iterations to converge.

December 2022 GLOBECOM 2022 - 2022 IEEE Global Communications Conference Conference

Deep Reinforcement Learning for Task Allocation in Energy Harvesting Mobile Crowdsensing

Sumedh Dongare, Andrea Patricia Ortiz Jimenez, Anja Klein

BibTeX DOI: 10.1109/GLOBECOM48099.2022.10001204

Abstract

Mobile crowd-sensing (MCS) is an upcoming sensing architecture which provides better coverage, accuracy, and requires lower costs than traditional wireless sensor networks. It utilizes a collection of sensors, or crowd, to perform various sensing tasks. As the sensors are battery operated and require a mechanism to recharge them, we consider energy harvesting (EH) sensors to form a sustainable sensing architecture. The execution of the sensing tasks is controlled by the mobile crowd-sensing platform (MCSP) which makes task allocation decisions, i.e., it decides whether or not to perform a task depending on the available resources, and if the task is to be performed, assigns it to suitable sensors. To make optimal allocation decisions, the MCSP requires perfect non-causal knowledge regarding the channel coefficients of the wireless links to the sensors, the amounts of energy the sensors harvest and the sensing tasks to be performed. However, in practical scenarios this non-causal knowledge is not available at the MCSP. To overcome this problem, we propose a novel Deep-Q-Network solution to find the task allocation strategy that maximizes the number of completed tasks using only realistic causal knowledge of the battery statuses of the available sensors. Through numerical evaluations we show that our proposed approach performs only 7.8% lower than the optimal solution. Moreover, it outperforms the myopically optimal and the random task allocation schemes.

December 2022 Schmalenbach Journal of Business Research Article

A friend in need is a friend indeed? Analysis of willingness to share self-produced electricity during a long-lasting power outage

Michèle Knodt, Carolin Bock, Anna Stöckl, Konstantin Kurz

PDF BibTeX DOI: 10.1007/s41471-022-00148-6

Abstract

Will private households owning a photovoltaic system share their electricity during a long-lasting power outage? Prior research has shown that our energy systems need to become more resilient by using dispersed energy sources—a role that could well be performed by these private photovoltaic systems, but only if their owners decide to share the produced electricity, and not consume it themselves. Considering the potential of this approach, it is indispensable to better understand incentives and motives that facilitate such cooperative behaviour. Drawing on theories of social dilemmas as well as prosocial behaviour, we hypothesize that both, structural solutions such as increased rewards as well as individual motives such as empathy-elicited altruism and norms predict cooperation. We test these hypotheses against a dataset of 80 households in Germany which were asked about their sharing behaviour towards four different recipient groups. We show that the effectiveness of motives differs significantly across recipient groups: Individual (intrinsic) motivations such as empathy-elicited altruism and altruistic norms serve as a strong predictor for cooperative behaviour towards related recipients as well as critical infrastructure, whereas higher rewards partially even reduce cooperation depending on the donor’s social value orientation. For the recipient groups neighbours and public infrastructure, no significant effect for any of the tested incentives is found. Contributing to literature on social dilemmas and energy resilience, these results demonstrate the relevance of individual rather than structural incentives for electricity sharing during a power outage to render our energy provision more resilient. Practical implications for policymakers are given.

December 2022 38th Annual Computer Security Applications Conference Conference

Ripples in the Pond: Transmitting Information through Grid Frequency Modulation

Jan Sebastian Götte, Liran Katzir, Björn Scheuermann

PDF BibTeX DOI: 10.1145/3564625.3564640

Abstract

The growing heterogenous ecosystem of networked consumer devices such as smart meters or IoT-connected appliances such as air conditioners is difficult to secure, unlike the utility side of the grid which can be defended effectively through rigorous IT security measures such as isolated control networks. In this paper, we consider a crisis scenario in which an attacker compromises a large number of consumer-side devices and modulates their electrical power to destabilize the grid and cause an electrical outage [9, 26, 27, 47, 50, 55]. In this paper propose a broadcast channel based on the modulation of grid frequency through which utility operators can issue commands to devices at the consumer premises both during an attack for mitigation and in its wake to aid recovery. Our proposed grid frequency modulation (GFM) channel is independent of other telecommunication networks. It is resilient towards localized blackouts and it is operational immediately after power is restored. Based on our GFM broadcast channel we propose a “safety reset” system to mitigate an ongoing attack by disabling a device’s network interfaces and resetting its control functions. It can also be used in the wake of an attack to aid recovery by shutting down non-essential loads to reduce strain on the grid. To validate our proposed design, we conducted simulations based on measured grid frequency behavior. Based on these simulations, we performed an experimental validation on simulated grid voltage waveforms using a smart meter equipped with a prototype safety reset system based on a commodity microcontroller.

December 2022 2022 Conference on Empirical Methods in Natural Language Processing Conference

The challenges of temporal alignment on Twitter during crisis

Aniket Pramanick, Tilman Beck, Kevin Stowe, Iryna Gurevych

PDF BibTeX

Abstract

Language use changes over time, and this impacts the effectiveness of NLP systems. This phenomenon is even more prevalent in social media data during crisis events where meaning and frequency of word usage may change over the course of days. Contextual language models fail to adapt temporally, emphasizing the need for temporal adaptation in models which need to be deployed over an extended period of time. While existing approaches consider data spanning large periods of time (from years to decades), shorter time spans are critical for crisis data. We quantify temporal degradation for this scenario and propose methods to cope with performance loss by leveraging techniques from domain adaptation. To the best of our knowledge, this is the first effort to explore effects of rapid language change driven by adversarial adaptations, particularly during natural and human-induced disasters. Through extensive experimentation on diverse crisis datasets, we analyze under what conditions our approaches outperform strong baselines while highlighting the current limitations of temporal adaptation methods in scenarios where access to unlabeled data is scarce.

November 2022 IEEE PES Innovative Smart Grid Technology (ISGT Europe 2022) Conference

Optimized UAV Placement for Resilient Crisis Communication and Power Grid Restoration

Michael Heise, Martin Pietsch, Florian Steinke, Maximilian Bauer, Burak Yilmaz

BibTeX DOI: 10.1109/ISGT-Europe54678.2022.9960494

Abstract

During crises where both communication networks and the electricity grid break down, restoring each individual infrastructure for disaster relief becomes generally infeasible. To tackle this challenge, we propose a disaster management solution using mobile ad-hoc networks (MANETs) formed by unmanned aerial vehicles (UAVs), offering a promising solution for emergency response. Apart from establishing emergency communications for rescue teams, UAV-enabled MANETs can also enable the formation of electrical microgrids based on distributed energy resources (DER) to locally restore the electric power. We determine the optimal locations and the number of UAVs for this purpose, taking the UAVs’ needs for repeated recharging into account. The problem is formulated on a discrete grid of potential places as a mixed-integer linear program (MILP) and solved via an accelerated feasibility query algorithm (FQA). The framework is evaluated for a toy-example and a modified version of the IEEE 123 node test feeder. Simulation results show that compared with conventional MILP approaches, the proposed FQA algorithm can significantly lower the computation times, particularly for large scale MANETs.

November 2022 IEEE PES Innovative Smart Grid Technology (ISGT Europe 2022) Conference

Monitoring Electricity Demand Synchronization Using Copulas

Tobias Gebhard, Florian Steinke, Eva Brucherseifer

BibTeX DOI: 10.1109/ISGT-Europe54678.2022.9960369

Abstract

Synchronization of the behavior of residential consumers, for example during crises, can lead to overloads in electric power grids. This holds especially for distribution grids, where the electrical infrastructure is not designed for the simultaneous high consumption of all households. Therefore, the monitoring and detection of (upcoming) synchronization trends is important. It is the basis for any countermeasures. We propose to model the dependency structure of consumer demands with a Gaussian copula using its correlation parameter as an indicator for synchronization. We then analyze the probability distribution of the aggregated load depending on the synchronization indicator. This allows us to infer the synchronization parameter from load measurements in real-time using a Bayesian approach. In simulation experiments with realistic household consumption distributions, we show how increased synchronization can be detected.

November 2022 Book

A European Perspective on Crisis Informatics: Citizens‘ and Authorities‘ Attitudes Towards Social Media for Public Safety and Security

Christian Reuter

PDF BibTeX DOI: 10.1007/978-3-658-39720-3

Abstract

Mobilising helpers in the event of a flood or letting friends know that you are okay in the event of a terrorist attack – more and more people are using social media in emergency, crisis or disaster situations. Storms, floods, attacks or pandemics (esp. COVID-19) show that citizens use social media to inform themselves or to coordinate. This book presents qualitative and quantitative studies on the attitudes of emergency services and citizens in Europe towards social media in emergencies. Across the individual sub-studies, almost 10,000 people are surveyed including representative studies in the Netherlands, Germany, the UK and Italy. The work empirically shows that social media is increasingly important for emergency services, both for prevention and during crises; that private use of social media is a driving force in shaping opinions for organisational use; and that citizens have high expectations towards authorities, especially monitoring social media is expected, and sometimes responses within one hour. Depending on the risk culture, the data show further differences, e.g. whether the state (Germany) or the individual (Netherlands) is seen as primarily responsible for coping with the situation.

November 2022 25th IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR 2021) Conference

3D Coverage Path Planning for Efficient Construction Progress Monitoring

Katrin Becker, Martin Oehler, Oskar von Stryk

BibTeX DOI: 10.1109/SSRR56537.2022.10018726

Abstract

On construction sites, progress must be monitored continuously to ensure that the current state corresponds to the planned state in order to increase efficiency, safety and detect construction defects at an early stage. Autonomous mobile robots can document the state of construction with high data quality and consistency. However, finding a path that fully covers the construction site is a challenging task as it can be large, slowly changing over time, and contain dynamic objects. Existing approaches are either exploration approaches that require a long time to explore the entire building, object scanning approaches that are not suitable for large and complex buildings, or planning approaches that only consider 2D coverage. In this paper, we present a novel approach for planning an efficient 3D path for progress monitoring on large construction sites with multiple levels. By making use of an existing 3D model we ensure that all surfaces of the building are covered by the sensor payload such as a 360-degree camera or a lidar. This enables the consistent and reliable monitoring of construction site progress with an autonomous ground robot. We demonstrate the effectiveness of the proposed planner on an artificial and a real building model, showing that much shorter paths and better coverage are achieved than with a traditional exploration planner.

November 2022 Chaos: An Interdisciplinary Journal of Nonlinear Science Article

Hypergraphon mean field games

Kai Cui, Wasiur R. KhudaBukhsh, Heinz Koeppl

BibTeX DOI: 10.1063/5.0093758

Abstract

We propose an approach to modeling large-scale multi-agent dynamical systems allowing interactions among more than just pairs of agents using the theory of mean field games and the notion of hypergraphons, which are obtained as limits of large hypergraphs. To the best of our knowledge, ours is the first work on mean field games on hypergraphs. Together with an extension to a multi-layer setup, we obtain limiting descriptions for large systems of non-linear, weakly interacting dynamical agents. On the theoretical side, we prove the well-foundedness of the resulting hypergraphon mean field game, showing both existence and approximate Nash properties. On the applied side, we extend numerical and learning algorithms to compute the hypergraphon mean field equilibria. To verify our approach empirically, we consider a social rumor spreading model, where we give agents intrinsic motivation to spread rumors to unaware agents, and an epidemic control problem. Recent developments in the field of complex systems have shown that real-world multi-agent systems are often not restricted to pairwise interactions, bringing to light the need for tractable models allowing higher-order interactions. At the same time, the complexity of analysis of large-scale multi-agent systems on graphs remains an issue even without considering higher-order interactions. An increasingly popular and tractable approach of analysis is the theory of mean field games. We combine mean field games with higher-order structure by means of hypergraphons, a limiting description of very large hypergraphs. To motivate our model, we build a theoretical foundation for the limiting system, showing that the limiting system has a solution and that it approximates finite, sufficiently large systems well. This allows us to analyze otherwise intractable, large hypergraph games with theoretical guarantees, which we verify using two examples of rumor spreading and epidemics control.

November 2022 25th IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR 2021) Conference

Multi-Cam ARM-SLAM: Robust Multi-Modal State Estimation Using Truncated Signed Distance Functions for Mobile Rescue Robots

Jasper Süß, Marius Schnaubelt, Oskar von Stryk

PDF BibTeX DOI: 10.1109/SSRR56537.2022.10018752

November 2022 Electric Power Systems Research Article

The water energy nexus: Improved emergency grid restoration with DERs

Martin Pietsch, Florian Steinke

BibTeX DOI: 10.1016/j.epsr.2022.108468

Abstract

Water networks as critical infrastructures typically feature emergency electricity generators for bridging short power blackouts. We propose to combine these black start capable generators with available distributed energy resources (DERs) in the power grid, often photovoltaic generation, to jointly restore both the electricity and the water grid in the case of emergencies. This is mutually beneficial for both notworks since common grid-following inverters of DERs cannot supply power without a grid-forming nucleus. We model both grids as a coupled graph and formulate a stochastic mixed-integer linear program to determine optimal switch placement and/or optimal switching sequences jointly for both networks. Limited fuel and power availabilities, grid-forming constraints, storages, and an even distribution of available resources are considered. By minimizing the number of switching devices and switching events we target manual operability. The proposed method extends the time that can be bridged until a full restoration of the main power grid is achieved. For a small example, we demonstrate that given enough solar radiation our solution allows us to extend the water supply duration by a factor of two, compared to using the emergency generators only for the water network, while additionally almost half of the electricity demands can be resupplied. Algorithmic scaling is validated with a combination of the IEEE 123-bus test feeder and the D-Town water network.

November 2022 25th International Conference on Intelligent Transportation Systems Conference

Unsupervised Driving Event Discovery Based on Vehicle CAN-data

Thomas Kreutz, Ousama Esbel, Max Mühlhäuser, Alejandro Sanchez Guinea

BibTeX DOI: 10.1109/ITSC55140.2022.9922158

Abstract

The data collected from a vehicle’s Controller Area Network (CAN) can quickly exceed human analysis or annotation capabilities when considering fleets of vehicles, which stresses the importance of unsupervised machine learning methods. This work presents a simultaneous clustering and segmentation approach for vehicle CAN-data that identifies common driving events in an unsupervised manner. The approach builds on self-supervised learning (SSL) for multivariate time series to distinguish different driving events in the learned latent space. We evaluate our approach with a dataset of real Tesla Model 3 vehicle CAN-data and a two-hour driving session that we annotated with different driving events. With our approach, we evaluate the applicability of recent time series-related contrastive and generative SSL techniques to learn representations that distinguish driving events. Compared to state-of-the-art (SOTA) generative SSL methods for driving event discovery, we find that contrastive learning approaches reach similar performance.

October 2022 30th European Signal Processing Conference Conference

False Discovery Rate Control for Grouped Variable Selection in High-Dimensional Linear Models Using the T-Knock Filter

Jasin Machkour, Michael Muma, Daniel P. Palomar

BibTeX DOI: 10.23919/EUSIPCO55093.2022.9909883

Abstract

High-dimensional variable selection is a challenging task, especially when groups of highly correlated variables are present in the data, such as in genomics research, direction-of-arrival estimation, and financial engineering. Recently, the T-Knock filter, a new framework for fast variable selection in high-dimensional settings has been developed. It provably controls the false discovery rate (FDR) at a given target level. However, its current version does not consider groups of highly correlated variables, which can lead to a loss in the true positive rate (TPR), i.e., the power. Hence, we propose the T-Knock+GVS filter that allows for grouped variable selection with FDR control in such settings. This is achieved by modifying the forward variable selection algorithm within the T- Knock filter and by adjusting the knockoff generation process such that the generated sets of knockoffs mimic the group correlation structure within the original set of variables. For a special case, we prove that the proposed T−Knock+GVS filter possesses the grouped variable selection property. Through a simulated high-dimensional genome-wide association study (GWAS), we show that the proposed method significantly increases the TPR, while controlling the FDR at the target level.

October 2022 30th European Signal Processing Conference Conference

Robust and Efficient Aggregation for Distributed Learning

Stefan Vlaski, Christian Schroth, Michael Muma, Abdelhak M. Zoubir

PDF BibTeX DOI: 10.23919/EUSIPCO55093.2022.9909822

Abstract

Distributed learning paradigms, such as federated and decentralized learning, allow for the coordination of models across a collection of agents, and without the need to exchange raw data. Instead, agents compute model updates locally based on their available data, and subsequently share the update model with a parameter server or their peers. This is followed by an aggregation step, which traditionally takes the form of a (weighted) average. Distributed learning schemes based on averaging are known to be susceptible to outliers. A single malicious agent is able to drive an averaging-based distributed learning algorithm to an arbitrarily poor model. This has motivated the development of robust aggregation schemes, which are based on variations of the median and trimmed mean. While such procedures ensure robustness to outliers and malicious behavior, they come at the cost of significantly reduced sample efficiency. This means that current robust aggregation schemes require significantly higher agent participation rates to achieve a given level of performance than their mean-based counterparts in non-contaminated settings. In this work we remedy this drawback by developing statistically efficient and robust aggregation schemes for distributed learning.

October 2022 Other

The Terminating-Random Experiments Selector: Fast High-Dimensional Variable Selection with False Discovery Rate Control

Jasin Machkour, Michael Muma, Daniel P. Palomar

PDF BibTeX DOI: 10.48550/arXiv.2110.06048

Abstract

We propose the Terminating-Random Experiments (T-Rex) selector, a fast variable selection method for high-dimensional data. The T-Rex selector controls a user-defined target false discovery rate (FDR) while maximizing the number of selected variables. This is achieved by fusing the solutions of multiple early terminated random experiments. The experiments are conducted on a combination of the original predictors and multiple sets of randomly generated dummy predictors. A finite sample proof based on martingale theory for the FDR control property is provided. Numerical simulations confirm that the FDR is controlled at the target level while allowing for a high power. We prove under mild conditions that the dummies can be sampled from any univariate probability distribution with finite expectation and variance. The computational complexity of the proposed method is linear in the number of variables. The T-Rex selector outperforms state-of-the-art methods for FDR control on a simulated genome-wide association study (GWAS), while its sequential computation time is more than two orders of magnitude lower than that of the strongest benchmark methods. The open source R package TRexSelector containing the implementation of the T-Rex selector is available on CRAN.

October 2022 Die Architekt Article

Zwischen Stadt und Land. Für eine neue Generation von Kulturlandschaften und Landschaftsräumen

Julia Kemkemer-Böhmer, Annette Rudolph-Cleff

PDF BibTeX

Abstract

Zwischen Stadt und Land werden nicht nur Abhängigkeiten, Verdrängungsprozesse und tiefe Einschnitte sichtbar, sondern auch Chancen, um auf die Herausforderungen des Klimawandels und der Urbanisierung zu antworten. Am Ende des Fortschrittsmythos ist es vielleicht möglich, über kulturelle Identitäten zwischen Stadt und Land nachzudenken, die nicht nur auf lokaler Tradition und individueller Erfahrung beruhen, sondern als zukunftsfähige Entwicklung im regionalen Wirtschaften und in kollektiver Verantwortung für den Natur- und Landschaftsraum gegründet sind. Welche Bilder haben wir für unseren Stadt- und Landschaftsraum?

October 2022 12th IEEE Global Humanitarian Technology Conference (GHTC 2022) Conference

Slums in Smart Cities - Rethink the Standard

John Friesen, Martin Pietsch

PDF BibTeX DOI: 10.1109/GHTC55712.2022.9911052

Abstract

Smart cities are often seen as a good way to meet the challenges of our time, such as population growth or climate change. The concepts to be developed in this context are often based on the assumption that the urban population is highly networked and adequately provided with infrastructure. At the same time, about one in four urban dwellers worldwide lives in a slum. Based on a literature review, we examine approaches that think smart cities and slums together. We show that smart cities approaches often do not take slums into account and that the standards assumed in these concepts should be rethought.

October 2022 IEEE Robotics and Automation Letters Article

A Modular and Portable Black Box Recorder for Increased Transparency of Autonomous Service Robots

Max Schmidt, Jérôme Kirchhoff, Oskar von Stryk

PDF BibTeX DOI: 10.1109/LRA.2022.3193633

Abstract

Autonomous service robots have great potential to support humans in tasks they cannot perform due to, amongst others, time constraints, work overload, or staff shortages. An important step for such service robots to be trusted or accepted by society is the provision of transparency. Its purpose is not only to communicate what a robot is doing according to the human interaction partners’ needs, it should also regard social and legal requirements. A black box recorder (inspired by flight recorders) increases the system’s transparency by facilitating the investigation of the cause of an incident, clarifying responsibilities, or improving the user’s understanding about the robot. In this work we propose the needed requirements of such a black box recorder for increased transparency of autonomous service robots, based on the related work. Further, we present a new modular and portable black box recorder design meeting these requirements. The applicability of the system is evaluated based on real-world robot data, using the realized open-source reference implementation.

September 2022 Sensors Article

Activity-Free User Identification Using Wearables Based on Vision Techniques

Alejandro Sanchez Guinea, Simon Heinrich, Max Mühlhäuser

BibTeX DOI: 10.3390/s22197368

Abstract

In order to achieve the promise of smart spaces where the environment acts to fulfill the needs of users in an unobtrusive and personalized manner, it is necessary to provide means for a seamless and continuous identification of users to know who indeed is interacting with the system and to whom the smart services are to be provided. In this paper, we propose a new approach capable of performing activity-free identification of users based on hand and arm motion patterns obtained from an wrist-worn inertial measurement unit (IMU). Our approach is not constrained to particular types of movements, gestures, or activities, thus, allowing users to perform freely and unconstrained their daily routine while the user identification takes place. We evaluate our approach based on IMU data collected from 23 people performing their daily routines unconstrained. Our results indicate that our approach is able to perform activity-free user identification with an accuracy of 0.9485 for 23 users without requiring any direct input or specific action from users. Furthermore, our evaluation provides evidence regarding the robustness of our approach in various different configurations

September 2022 24th International Conference on Human-Computer Interaction with Mobile Devices and Services Conference

Comparing VR Exploration Support for Ground-Based Rescue Robots

Julius von Willich, Andrii Matviienko, Sebastian Günther, Max Mühlhäuser

BibTeX DOI: 10.1145/3528575.3551440

Abstract

Rescue robots have been extensively used in crisis situations for exploring dangerous areas. This exploration is usually facilitated via a remote operation by the rescue team. Although Virtual Reality (VR) was proposed to facilitate remote control due to its high level of immersion and situation awareness, we still lack intuitive and easy-to-use operation modes for search and rescue teams in VR environments. In this work, we propose four operation modes for ground-based rescue robots to utilize an efficient search and rescue: (a) Handle Mode, (b) Lab Mode, (c) Remote Mode, and (d) UI Mode. We evaluated these operation modes in a controlled lab experiment (N = 8) in terms of robot collisions, number of rescued victims, and mental load. Our results indicate that control modes with robot automation (UI and Remote mode) outperform modes with full control given to participants. In particular, we discovered that UI and Remote Mode lead to the lowest number of collisions, driving time, visible victims remaining, rescued victims, and mental load.

September 2022 Sustainability Article

Financial-Return Crowdfunding for Energy and Sustainability in the German-Speaking Realm

Sven Siebeneicher, Ilker Yenice, Carolin Bock

BibTeX DOI: 10.3390/su141912239

September 2022 Mensch und Computer 2022: Facing Realities Conference

Perceptions and Use of Warning Apps - Did Recent Crises Lead to Changes in Germany?

Christian Reuter, Jasmin Haunschild, Marc-André Kaufhold

BibTeX DOI: 10.1145/3543758.3543770

Abstract

Warning and emergency apps are an integral part of crisis informatics and particularly relevant in countries that currently do not have cell broadcast, such as Germany. Previous studies have shown that such apps are regarded as relevant, but only around 16% of German citizens used them in 2017 and 2019. With the COVID-19 pandemic and a devastating flash flood, Germany has recently experienced severe crisis-related losses. By comparing data from representative surveys from 2017, 2019 and 2021, this study investigates whether these events have changed the perceptions of warning apps and their usage patterns in Germany. The study shows that while multi-hazard emergency and warning apps have been easily surpassed in usage by COVID-19 contact tracing apps, the use of warning apps has also increased and the pandemic has added new desired features. While these have been little-used during the COVID-19 pandemic, especially non-users see smartphone messengers app channels as possible alternatives to warning apps. In addition, regional warning apps appear promising, possibly because they make choosing a warning app easier when there are several available on the market.

September 2022 ACM Transactions on Sensor Networks Article

LoRaWAN Security: An Evolvable Survey on Vulnerabilities, Attacks and their Systematic Mitigation

Frank Hessel, Lars Almon, Matthias Hollick

BibTeX DOI: 10.1145/3561973

Abstract

The changing vulnerability and threat landscape constantly challenge the security of wireless communication standards and protocols. For the Internet of Things (IoT), LoRaWAN is one of the dominant technologies for urban environments, industrial settings, or critical infrastructures due to its low-power and long-range capabilities. LoRaWAN IoT deployments are expected to operate for multiple years or even decades. Hence, it is imperative to maintain operational security at all times while continuously evolving the security of the standard and its implementations. We survey LoRaWAN security and follow a systematic and evolvable approach that can be dynamically updated. To this end, we propose a novel methodology to create evolvable surveys which relate the analyzed critical security concepts, thus allowing IT security experts to reason about LoRaWAN security properties over time. With this, we provide a tool to hardware manufacturers, software developers and providers, and network operators to achieve sustainable security for IoT deployments.

September 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society Conference

Fast and Sample Accurate R-Peak Detection for Noisy ECG Using Visibility Graphs

Taulant Koka, Michael Muma

BibTeX DOI: 10.1109/EMBC48229.2022.9871266

Abstract

More than a century has passed since Einthoven laid the foundation of modern electrocardiography and in recent years, driven by the advance of wearable and low budget devices, a sample accurate detection of R-peaks in noisy ECG-signals has become increasingly important. To accommodate these demands, we propose a new R-peak detection approach that builds upon the visibility graph transformation, which maps a discrete time series to a graph by expressing each sample as a node and assigning edges between intervisible samples. The proposed method takes advantage of the high connectivity of large, isolated values to weight the original signal so that R-peaks are amplified while other signal components and noise are suppressed. A simple thresholding procedure, such as the widely used one by Pan and Tompkins, is then sufficient to accurately detect the R-peaks. The weights are computed for overlapping segments of equal size and the time complexity is shown to be linear in the number of segments. Finally, the method is benchmarked against existing methods using the same thresholding on a noisy and sample accurate database. The results illustrate the potential of the proposed method, which outperforms common detectors by a significant margin.

September 2022 ISNGI - the International Symposium for Next Generation Infrastructure Conference

Resilient data platforms for smart cities

Michaela Leštáková, Yasin Alhamwy, Frank Hessel, Kevin Logan, Andreas Morgen, Martin Pietsch

PDF BibTeX

September 2022 The 39th International Conference on Machine Learning Conference

IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages

Emanuele Bugliarello, Fangyu Liu, Jonas Pfeiffer, Siva Reddy, Desmond Elliott, Edoardo M. Ponti, Ivan Vulić

PDF BibTeX

Abstract

Reliable evaluation benchmarks designed for replicability and comprehensiveness have driven progress in machine learning. Due to the lack of a multilingual benchmark, however, vision-and-language research has mostly focused on English language tasks. To fill this gap, we introduce the Image-Grounded Language Understanding Evaluation benchmark. IGLUE brings together — by both aggregating pre-existing datasets and creating new ones — visual question answering, cross-modal retrieval, grounded reasoning, and grounded entailment tasks across 20 diverse languages. Our benchmark enables the evaluation of multilingual multimodal models for transfer learning, not only in a zero-shot setting, but also in newly defined few-shot learning setups. Based on the evaluation of the available state-of-the-art models, we find that translate-test transfer is superior to zero-shot transfer and that few-shot learning is hard to harness for many tasks. Moreover, downstream performance is partially explained by the amount of available unlabelled textual data for pretraining, and only weakly by the typological distance of target – source languages. We hope to encourage future research efforts in this area by releasing the benchmark to the community.

August 2022 47th Conference on Local Computer Networks Conference

ForestEdge: Unobtrusive Mechanism Interception in Environmental Monitoring

Patrick Lampe, Markus Sommer, Artur Sterz, Jonas Höchst, Christian Uhl, Bernd Freisleben

BibTeX DOI: 10.1109/LCN53696.2022.9843426

Abstract

A network for environmental monitoring typically requires a large number of sensors. If a longer service life is intended, it is essential that the deployed sensor systems can be upgraded without modifying hardware. Often, these networks rely on proprietary hardware/software components tailored to the desired functionality, but these could technically also be used for other applications. We present a demo of mechanism interception, a novel approach to unobtrusively add or modify the functionality of an existing networked system, in our case a TreeTalker, without touching any proprietary components. We demonstrate how a cloud infrastructure can be unobtrusively replaced by an edge infrastructure in a wireless sensor network. Our results indicate that mechanism interception is a compelling approach for our scenario to provide previously unavailable functionality without modifying existing components.

August 2022 2022 IEEE International Conference on Communications Conference

Delay- and Incentive-Aware Crowdsensing: A Stable Matching Approach for Coverage Maximization

Bernd Simon, Sumedh Dongare, Tobias Mahn, Andrea Patricia Ortiz Jimenez, Anja Klein

BibTeX DOI: 10.1109/ICC45855.2022.9838603

Abstract

Mobile crowdsensing (MCS) is a novel approach to increase the coverage, lower the costs, and increase the accuracy of sensing data. Its main idea is to collect sensor data using mobile units (MUs). The sensing is controlled by a mobile crowdsensing platform (MCSP) through the assignment of delay-sensitive sensing tasks to the MUs. Although promising, research effort in MCS is still needed to find task assignment solutions that maximize the coverage while considering the cost incurred by the MCSPs, the preferences of the MUs and the limited communication resources available. Specifically, we identify two main challenges: (i) A task assignment problem which incorporates the MCSP’s utility and the preferences of the MUs. (ii) An underlying communication resource allocation problem formulating the requirement of the timely transmission of sensing results given the limited communication resources. To address these challenges, we propose a novel two-stage matching algorithm. In the first stage, potential MU-task pairs are constructed considering the preferences of the MUs and the utility of the MCSP. In the second stage, the communication resource allocation is done based on potential MU-task pairs from the first stage. Through numerical simulations, we show that our proposed approach outperforms state-of-the-art methods in terms of the MCSP’s utility, coverage and MU’s satisfaction.

August 2022 2022 IEEE International Conference on Communications Conference

A Global Optimization Method for Energy-Minimal UAV-Aided Data Collection over Fixed Flight Path

Guangping Lu, Jing Zhang, Lin Xiang, Xiaohu Ge

PDF BibTeX DOI: 10.1109/ICC45855.2022.9838554

Abstract

This paper considers optimal resource allocation for data collection from multiple ground devices (GDs) using a rotary-wing unmanned aerial vehicle (UAV). The UAV’s flight path, i.e., the sequence of moving positions, is given a priori due to requirements of e.g. patrol and inspection missions, whereas the UAV’s trajectory, i.e., the path and time schedule of movement, remains dependent on its hovering positions and flying speeds along the path. To improve the spectral and energy efficiency of the GDs, the UAV employs a directional antenna and performs wireless power transfer (WPT) to the GDs before collecting data from them. We jointly optimize the UAV’s flying speeds, hovering locations, and radio resource allocation (including time, bandwidth and transmit power) for minimization of the total energy consumption of the UAV required for completing data collection along the flying path. We show that given any flight path, the propulsion energy consumption of the UAV is a convex function of the flight speeds. However, due to the highly directive transmission, communication and flight of the UAV become strongly coupled and complicates the problem, e.g. the selection of the UAV’s hovering points will affect both the order of serving the GDs and the antenna gain of the UAV. Moreover, nonconvexity in the flight path constraints further obscures an efficient solution to the resource allocation problem. To tackle these challenges, we propose an iterative algorithm based on the branch-and-bound (BnB) method, which can obtain the globally optimal solution when the flight path coincides with the boundary of a convex set. Simulation results show that compared with several baseline algorithms, the proposed algorithm can significantly lower the energy consumption of the UAV during data collection.

August 2022 Computer und Recht Article

Datensouveränität im Spannungsfeld zwischen Datenschutz und Datennutzung: das Beispiel des Data Governance Acts

Gerrit Hornung, Sabrina Schomberg

BibTeX DOI: 10.9785/cr-2022-380810

August 2022 2nd International Workshop on Human-Robot Interaction new Trends in Service and Industry (HRI-SI2022) Conference

Commonsense Knowledge for Human-Robot Collaboration in Hybrid Search and Rescue Teams

Kurt Geihs, Stefan Jakob, Stephan Opfer, Yasin Alhamwy

PDF BibTeX

August 2022 IEEE/ACM Transactions on Networking Article

Multi-Stakeholder Service Placement via Iterative Bargaining With Incomplete Information

Artur Sterz, Patrick Felka, Bernd Simon, Sabrina Klos, Anja Klein, Oliver Hinz, Bernd Freisleben

PDF BibTeX DOI: 10.1109/TNET.2022.3157040

Abstract

Mobile edge computing based on cloudlets is an emerging paradigm to improve service quality by bringing computation and storage facilities closer to end users and reducing operating cost for infrastructure providers (IPs) and service providers (SPs). To maximize their individual benefits, IP and SP have to reach an agreement about placing and executing services on particular cloudlets. We show that a Nash Bargaining Solution (NBS) yields the optimal solution with respect to social cost and fairness if IP and SP have complete information about the parameters of their mutual cost functions. However, IP and SP might not be willing or able to share all information due to business secrets or technical limitations. Therefore, we present a novel iterative bargaining approach without complete mutual information to achieve substantial cost reductions for both IP and SP. Furthermore, we investigate how different degrees of information sharing impact social cost and fairness of the different approaches. Our evaluation based on the mobile augmented reality game Ingress shows that our approach achieves up to about 82% of the cost reduction that the NBS achieves and a cost reduction of up to 147% compared to traditional Take-it-or-Leave-it approaches, despite incomplete information.

July 2022 SIGGRAPH ‘22: Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference

Immersive-Labeler: Immersive Annotation of Large-Scale 3D Point Clouds in Virtual Reality

Achref Doula, Tobias Güdelhöfer, Andrii Matviienko, Max Mühlhäuser, Alejandro Sanchez Guinea

BibTeX DOI: 10.1145/3532719.3543249

Abstract

We present Immersive-Labeler, an environment for the annotation of large-scale 3D point cloud scenes of urban environments. Our concept is based on the full immersion of the user in a VR-based environment that represents the 3D point cloud scene while offering adapted visual aids and intuitive interaction and navigation modalities. Through a user-centric design, we aim to improve the annotation experience and thus reduce its costs. For the preliminary evaluation of our environment, we conduct a user study (N=20) to quantify the effect of higher levels of immersion in combination with the visual aids we implemented on the annotation process. Our findings reveal that higher levels of immersion combined with object-based visual aids lead to a faster and more engaging annotation process.

July 2022 Academy of Management proceedings Article

Sustainable aim or personal gain? Effects of Personal and Sustainable Value on Crowdfunding Success

Sven Siebeneicher, Carolin Bock

PDF BibTeX DOI: 10.5465/AMBPP.2022.14306abstract

Abstract

We extend the entrepreneurship literature on crowdfunding by investigating how the relation between personal value and shared sustainable value affects crowdfunding success. To define shared sustainable value, we disaggregate sustainability into three interrelated dimensions: ecologic value, economic value, and social value. Relying on signaling theory, we identify the value proposed in campaign teasers and descriptions by deriving and utilizing reliable word lists for text analysis. Our findings suggest that “first impression matters”. Teasers and descriptions both have significant effects, and crowdfunding can be employed to foster altruistic behavior. For our analysis, we rely on a sample of 45,608 Kickstarter campaigns.

July 2022 Academy of Management Proceedings Article

Flip the Tweet - The Two-Sided Coin of Entrepreneurial Empathy

Carolin Bock, Konstantin Kurz

PDF BibTeX DOI: 10.5465/AMBPP.2022.14076abstract

Abstract

Is empathy merely a good thing for entrepreneurs? In contrast to the hitherto predominantly positive view in entrepreneurship literature, psychology and management scholars have recently adopted a more critical perspective. Transferring their findings, we provide a novel “too-much-of-a-good-thing” perspective on entrepreneurial empathy. We test our model against a dataset of 4,725 real entrepreneurs and demonstrate that empathy influences opportunity recognition, evaluation and exploitation in an inverted U-shaped pattern. We also show that these exploited opportunities then lead to higher amounts of achieved financial resources. These findings provide strong evidence for considering entrepreneurial empathy an important but highly ambiguous success factor.

July 2022 44th International Conference on Software Engineering Conference

Change is the Only Constant: Dynamic Updates for Workflows

Daniel Sokolowski, Pascal Weisenburger, Guido Salvaneschi

BibTeX DOI: 10.1145/3510003.3510065

Abstract

Software systems must be updated regularly to address changing requirements and urgent issues like security-related bugs. Traditionally, updates are performed by shutting down the system to replace certain components. In modern software organizations, updates are increasingly frequent—up to multiple times per day—hence, shutting down the entire system is unacceptable. Safe dynamic software updating (DSU) enables component updates while the system is running by determining when the update can occur without causing errors. Safe DSU is crucial, especially for long-running or frequently executed asynchronous transactions (workflows), e.g., user-interactive sessions or order fulfillment processes. Unfortunately, previous research is limited to synchronous transaction models and does not address this case.In this work, we propose a unified model for safe DSU in workflows. We discuss how state-of-the-art DSU solutions fit into this model and show that they incur significant overhead. To improve the performance, we introduce Essential Safety, a novel safe DSU approach that leverages the notion of non-essential changes, i.e., semantics preserving updates. In 106 realistic BPMN workflows, Essential Safety reduces the delay of workflow completions, on average, by 47.8 compared to the state of the art. We show that the distinction of essential and non-essential changes plays a crucial role in this reduction and that, as suggested in the literature, non-essential changes are frequent: at least 60 and often more than 90 of systems’ updates in eight monorepos we analyze.

July 2022 IEEE Transactions on Vehicular Technology Article

Scheduling for Massive MIMO with Hybrid Precoding using Contextual Multi-Armed Bandits

Weskley V. F. Mauricio, Tarcisio Ferreira Maciel, Anja Klein, Francisco Rafael Marques Lima

PDF BibTeX DOI: 10.1109/TVT.2022.3166654

Abstract

In this work we study different scheduling problems in the downlink of a Frequency Division Duplex multiuser wireless system that employs a hybrid precoding antenna architecture for massive Multiple Input Multiple Output. In this context, we propose a scheduling framework using Reinforcement Learning (RL) tools, namely Contextual Multi-Armed Bandits (CMAB), that can dynamically adapt themselves to solve three scheduling problems, which are: i) Maximum Throughput (MT); ii) Maximum Throughput with Fairness Guarantees (MTFG), and; iii) Maximum Throughput with QoS Guarantees (MTQG), which are well-known relevant problems. Before performing scheduling itself, we exploit statistical Channel State Information (CSI) to create clusters of spatially compatible User Equipmentss (UEss). This structure, combined with the usage of Zero-Forcing precoding, allows us to reduce the scheduler complexity by considering each cluster as an independent virtual RL scheduling agent. Next, we apply a new learning-based scheduler aiming to optimize the desired system performance metric. Moreover, only scheduled UEss need to feed back instantaneous equivalent CSI, which also reduces the signaling overhead of the proposal. The superiority of the proposed framework is demonstrated through numerical simulations in comparison with reference solutions.

June 2022 36th European Conference on Object-Oriented Programming Conference

Functional Programming for Distributed Systems with XC

Giorgio Audrito, Roberto Casadei, Ferruccio Damiani, Guido Salvaneschi, Mirko Viroli

PDF BibTeX DOI: 10.4230/LIPIcs.ECOOP.2022.20

Abstract

Programming distributed systems is notoriously hard due to - among the others - concurrency, asynchronous execution, message loss, and device failures. Homogeneous distributed systems consist of similar devices that communicate to neighbours and execute the same program: they include wireless sensor networks, network hardware, and robot swarms. For the homogeneous case, we investigate an experimental language design that aims to push the abstraction boundaries farther, compared to existing approaches. In this paper, we introduce the design of XC, a programming language to develop homogeneous distributed systems. In XC, developers define the single program that every device executes and the overall behaviour is achieved collectively, in an emergent way. The programming framework abstracts over concurrency, asynchronous execution, message loss, and device failures. We propose a minimalistic design, which features a single declarative primitive for communication, state management, and connection management. A mechanism called alignment enables developers to abstract over asynchronous execution while still retaining composability. We define syntax and operational semantics of a core calculus, and briefly discuss its main properties. XC comes with two DSL implementations: a DSL in Scala and one in C++. An evaluation based on smart-city monitoring demonstrates XC in a realistic application.

June 2022 Other

RIS assisted device activity detection with statistical channel state information

Friedemann Laue, Vahid Jamali, Robert Schober

PDF BibTeX DOI: 10.48550/arXiv.2206.06805

Abstract

This paper studies reconfigurable intelligent surface (RIS) assisted device activity detection for grant-free (GF) uplink transmission in wireless communication networks. In particular, we consider mobile devices located in an area where the direct link to an access point (AP) is blocked. Thus, the devices try to connect to the AP via a reflected link provided by an RIS. Therefore, a RIS phase-shift design is desired that covers the entire blocked area with a wide reflection beam because the exact locations and times of activity of the devices are unknown in GF transmission. In order to study the impact of the phase-shift design on the device activity detection, we derive a generalized likelihood ratio test (GLRT) based detector and present an analytical expression for the probability of detection. Assuming knowledge of statistical CSI, we formulate an optimization problem for the phase-shift design for maximization of the guaranteed probability of detection for all locations within a given coverage area. To tackle the non-convexity of the problem, we propose two different approximations of the objective function. The first approximation leads to a design that aims to reduce the variations of the end-to-end channel while taking system parameters such as transmit power, noise power, and probability of false alarm into account. The second approximation can be adopted for versatile RIS deployments because it only depends on the line-of-sight component of the end-to-end channel and is not affected by system parameters. For comparison, we also consider a phase-shift design maximizing the average channel gain and a baseline analytical phase-shift design for large blocked areas. Our performance evaluation shows that the proposed approximations result in phase-shift designs that guarantee high probability of detection across the coverage area and outperform the baseline designs.

June 2022 Fluid Power: Digital, Reliable, Sustainable - 13th International Fluid Power Conference Conference

Data Management as an Enabler of Sustainability – Discussion Using the Example of a Digital Data Sheet

Kevin Logan, Marvin Meck, N. Preuß, Philipp Wetterich, P. F. Pelz

PDF BibTeX DOI: 10.18154/RWTH-2023-04624

Abstract

Sustainable systems require sustainable data. Imposing the paradigm of sustainability on data corresponds to making it findable, accessible, interoperable and reusable (FAIR). This is vital for planning a fluid system fulfilling functionality as well as satisfactory quality. Therefore, customers are dependent on component suppliers making FAIR product data available. Rather than assuming the context of runtime machine to machine communication and service-oriented interfaces, i.e., solutions based on digital twins, the presented work proposes a digital data sheet for components based on citation of information models compliant with W3C standards. This enables suppliers to provide comprehensible and transparent data and customers to implement sustainability already during the design process of fluid systems.

June 2022 Datenschutz und Datensicherheit Article

Kollaboration im Datenschutz: Collaboration Engineering als Instrument zur partizipativen und nachhaltigen Gestaltung von Datenschutzprozessen

Gerrit Hornung, Matthias Söllner, Jan-Philipp Stroscher, Eva-Maria Zahn

PDF BibTeX DOI: 10.1007/s11623-022-1625-4

Abstract

Die DSGVO enthält sehr unterschiedliche Vorgaben zur Zusammenarbeit verschiedener Akteure, aber kein systematisches Modell für die Ausgestaltung dieser Formen der Zusammenarbeit. Gerade für KMU könnten standardisierte Verfahren ein Ansatz zur effizienten Umsetzung sein. Der Beitrag untersucht die Frage, an welchen Stellen Ansätze des Collaboration Engineering Zusammenarbeitsprozesse ermöglichen würden, die die gesetzlichen Vorgaben mit Leben füllen.

May 2022 Cambridge Handbook of Cyber Behavior Other

Crisis Informatics

Christian Reuter, Marc-André Kaufhold

BibTeX

May 2022 19th International Conference on Information: Systems for Crisis Response and Management (ISCRAM 2022) Conference

CAMON: Aerial-Ground Cooperation System for Disaster Network Detection

Julian Zobel, Ralf Kundel, Ralf Steinmetz

PDF BibTeX

May 2022 60th Annual Meeting of the Association for Computational Linguistics Conference

AdapterHub Playground: Simple and Flexible Few-Shot Learning with Adapters

Tilman Beck, Bela Bohlender, Christina Viehmann, Vincent Hane, Yanik Adamson, Jaber Khuri, Jonas Brossmann, Jonas Pfeiffer, Iryna Gurevych

PDF BibTeX

May 2022 60th Annual Meeting of the Association for Computational Linguistics Conference

UKP-SQUARE: An Online Platform for Question Answering Research

Tim Baumgärtner, Kexin Wang, Rachneet Sachdeva, Gregor Geigle, Max Eichler, Clifton Poth, Hannah Sterz, Haritz Puerto, Leonardo F. R. Ribeiro, Jonas Pfeiffer, Nils Reimers, Gözde Gül Şahin, Iryna Gurevych

PDF BibTeX

May 2022 10th Edition of the International Conference on Networked Systems (NETYS 2022) Conference

Bird@Edge: Bird Species Recognition at the Edge

Jonas Höchst, Hicham Bellafkir, Patrick Lampe, Markus Vogelbacher, Markus Mühling, Daniel Schneider, Kim Lindner, Sascha Rösner, Dana G. Schabo, Nina Farwig, Bernd Freisleben

PDF BibTeX DOI: 10.1007/978-3-031-17436-0_6

Abstract

We present Bird@Edge, an Edge AI system for recognizing bird species in audio recordings to support real-time biodiversity monitoring. Bird@Edge is based on embedded edge devices operating in a distributed system to enable efficient, continuous evaluation of soundscapes recorded in forests. Multiple ESP32-based microphones (called Bird@Edge Mics) stream audio to a local Bird@Edge Station, on which bird species recognition is performed. The results of several Bird@Edge Stations are transmitted to a backend cloud for further analysis, e.g., by biodiversity researchers. To recognize bird species in soundscapes, a deep neural network based on the EﬀicientNet-B3 architecture is trained and optimized for execution on embedded edge devices and deployed on a NVIDIA Jetson Nano board using the DeepStream SDK. Our experiments show that our deep neural network outperforms the state-of-the-art BirdNET neural network on several data sets and achieves a recognition quality of up to 95.2% mean average precision on soundscape recordings in the Marburg Open Forest, a research and teaching forest of the University of Marburg, Germany. Measurements of the power consumption of the Bird@Edge components highlight the real-world applicability of the approach. All software and firmware components of Bird@Edge are available under open source licenses.

May 2022 60th Annual Meeting of the Association for Computational Linguistics Conference

xGQA: Cross-Lingual Visual Question Answering

Jonas Pfeiffer, Gregor Geigle, Aishwarya Kamath, Jan-Martin Steitz, Stefan Roth, Ivan Vulić, Iryna Gurevych

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May 2022 22nd Power Systems Computation Conference (PSCC 2022) Conference

The Water Energy Nexus: Improved Emergency Grid Restoration with DERs

Martin Pietsch, Florian Steinke

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Abstract

Water networks as critical infrastructures typically feature emergency electricity generators for bridging short power blackouts. We propose to combine these black start capable generators with available distributed energy resources (DERs) in the power grid, often photovoltaic generation, to jointly restore both the electricity and the water grid after blackouts. This is mutually beneficial for both networks since common grid- following inverters of DERs cannot supply power without a grid- forming nucleus. We model both grids as a coupled graph and formulate a stochastic mixed-integer linear program to determine optimal switch placement and/or switching sequences. Limited fuel and power availabilities, grid-forming constraints, storages, and an even distribution of available resources are considered. By minimizing the number of switching devices and switching events we target manual operability. The proposed method extends the time that can be bridged until a full restoration of the main power grid is achieved. For a small example, we demonstrate that given enough solar radiation our solution can double the water supply duration compared to using the generators only for the water network, while additionally resupplying almost half of the electricity demand. Algorithmic scaling is validated with a combination of the IEEE 123-bus test feeder and the D-Town water network.

May 2022 Transactions of the Association for Computational Linguistics Article

Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval

Gregor Geigle, Jonas Pfeiffer, Nils Reimers, Ivan Vulić, Iryna Gurevych

PDF BibTeX DOI: 10.1162/tacl_a_00473

Abstract

Current state-of-the-art approaches to cross- modal retrieval process text and visual input jointly, relying on Transformer-based architectures with cross-attention mechanisms that attend over all words and objects in an image. While offering unmatched retrieval performance, such models: 1) are typically pretrained from scratch and thus less scalable, 2) suffer from huge retrieval latency and inefficiency issues, which makes them impractical in realistic applications. To address these crucial gaps towards both improved and efficient cross- modal retrieval, we propose a novel fine-tuning framework that turns any pretrained text-image multi-modal model into an efficient retrieval model. The framework is based on a cooperative retrieve-and-rerank approach that combines: 1) twin networks (i.e., a bi-encoder) to separately encode all items of a corpus, enabling efficient initial retrieval, and 2) a cross-encoder component for a more nuanced (i.e., smarter) ranking of the retrieved small set of items. We also propose to jointly fine- tune the two components with shared weights, yielding a more parameter-efficient model. Our experiments on a series of standard cross-modal retrieval benchmarks in monolingual, multilingual, and zero-shot setups, demonstrate improved accuracy and huge efficiency benefits over the state-of-the-art cross- encoders.

May 2022 IEEE Transactions on Vehicular Technology Article

Age of Information Modeling and Optimization for Fast Information Dissemination in Vehicular Social Networks

Zipeng Li, Lin Xiang, Xiaohu Ge

PDF BibTeX DOI: 10.1109/TVT.2022.3154766

May 2022 Journal of Process Control Article

Closed-loop real-time optimization for unsteady operating production systems

Andreas Himmel, Rolf Findeisen, Kai Sundmacher

BibTeX DOI: 10.1016/j.jprocont.2022.03.002

April 2022 2022 CHI Conference on Human Factors in Computing Systems Conference

BikeAR: Understanding Cyclists’ Crossing Decision-Making at Uncontrolled Intersections using Augmented Reality

Andrii Matviienko, Florian Müller, Dominik Schön, Paul Seesemann, Sebastian Günther, Max Mühlhäuser

BibTeX DOI: 10.1145/3491102.3517560

Abstract

Cycling has become increasingly popular as a means of transportation. However, cyclists remain a highly vulnerable group of road users. According to accident reports, one of the most dangerous situations for cyclists are uncontrolled intersections, where cars approach from both directions. To address this issue and assist cyclists in crossing decision-making at uncontrolled intersections, we designed two visualizations that: (1) highlight occluded cars through an X-ray vision and (2) depict the remaining time the intersection is safe to cross via a Countdown. To investigate the efficiency of these visualizations, we proposed an Augmented Reality simulation as a novel evaluation method, in which the above visualizations are represented as AR, and conducted a controlled experiment with 24 participants indoors. We found that the X-ray ensures a fast selection of shorter gaps between cars, while the Countdown facilitates a feeling of safety and provides a better intersection overview.

April 2022 CHI ‘22: CHI Conference on Human Factors in Computing Systems Conference

SkyPort: Investigating 3D Teleportation Methods in Virtual Environments

Andrii Matviienko, Florian Müller, Martin Schmitz, Marco Fendrich, Max Mühlhäuser

BibTeX DOI: 10.1145/3491102.3501983

Abstract

Teleportation has become the de facto standard of locomotion in Virtual Reality (VR) environments. However, teleportation with parabolic and linear target aiming methods is restricted to horizontal 2D planes and it is unknown how they transfer to the 3D space. In this paper, we propose six 3D teleportation methods in virtual environments based on the combination of two existing aiming methods (linear and parabolic) and three types of transitioning to a target (instant, interpolated and continuous). To investigate the performance of the proposed teleportation methods, we conducted a controlled lab experiment (N = 24) with a mid-air coin collection task to assess accuracy, efciency and VR sickness. We discovered that the linear aiming method leads to faster and more accurate target selection. Moreover, a combination of linear aiming and instant transitioning leads to the highest efciency and accuracy without increasing VR sickness.

April 2022 2022 CHI Conference on Human Factors in Computing Systems Conference

VR-Surv: A VR-Based Privacy Preserving Surveillance System

Achref Doula, Alejandro Sanchez Guinea, Max Mühlhäuser

BibTeX DOI: 10.1145/3491101.3519645

Abstract

The recent advances in smart city infrastructure have provided support for a higher adoption of surveillance cameras as a mainstream crime prevention measure. However, a consequent massive deployment raises concerns about privacy issues among citizens. In this paper, we present VR-Surv, a VR-based privacy aware surveillance system for large scale urban environments. Our concept is based on conveying the semantics of the scene uniquely, without revealing the identity of the individuals or the contextual details that might violate the privacy of the entities present in the surveillance area. For this, we create a virtual replica of the areas of interest, in real-time, through the combination of procedurally generated environments and markerless motion capture models. The results of our preliminary evaluation revealed that our system successfully conceals privacy-sensitive data, while preserving the semantics of the scene. Furthermore, participants in our user study expressed higher acceptance to being surveilled through the proposed system.

April 2022 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Conference

Effects of the Level of Detail on the Recognition of City Landmarks in Virtual Environments

Achref Doula, Philipp Kaufmann, Alejandro Sanchez Guinea, Max Mühlhäuser

PDF BibTeX DOI: 10.1109/VRW55335.2022.00281

Abstract

The reconstruction of city landmarks is central to creating recognizable virtual environments representing real cities. Despite the recent advances, it is still not clear what level of detail (LOD) to adopt when reconstructing those landmarks for their correct recognition, and if particular architectural styles represent specific challenges in this respect. In this paper, we investigate the effect of LOD on landmark recognition, generally, and on some architectural styles, specifically. The results of our user study show that higher LOD lead to a better landmark identification. Particularly, Neoclassical-style buildings need more details to be individually distinguished from similar ones.

April 2022 International Journal of Disaster Risk Reduction Article

Crisis communication in a blackout scenario - An assessment considering socio-spatial parameters and the vulnerabilities of the population

Annette Rudolph-Cleff, Michèle Knodt, Joachim Schulze, Alice Engel

PDF BibTeX DOI: 10.1016/j.ijdrr.2022.102856

April 2022 Phys. Rev. E Article

Motif-based mean-field approximation of interacting particles on clustered networks

Kai Cui, Wasiur R. KhudaBukhsh, Heinz Koeppl

PDF BibTeX DOI: 10.1103/PhysRevE.105.L042301

April 2022 10th International Conference on Learning Representations (ICLR 2022) Conference

Learning Graphon Mean Field Games and Approximate Nash Equilibria

K. Cui, H. Koeppl

BibTeX

Abstract

Recent advances at the intersection of dense large graph limits and mean field games have begun to enable the scalable analysis of a broad class of dynamical sequential games with large numbers of agents. So far, results have been largely limited to graphon mean field systems with continuous-time diffusive or jump dynamics, typically without control and with little focus on computational methods. We propose a novel discrete-time formulation for graphon mean field games as the limit of non-linear dense graph Markov games with weak interaction. On the theoretical side, we give extensive and rigorous existence and approximation properties of the graphon mean field solution in sufficiently large systems. On the practical side, we provide general learning schemes for graphon mean field equilibria by either introducing agent equivalence classes or reformulating the graphon mean field system as a classical mean field system. By repeatedly finding a regularized optimal control solution and its generated mean field, we successfully obtain plausible approximate Nash equilibria in otherwise infeasible large dense graph games with many agents. Empirically, we are able to demonstrate on a number of examples that the finite-agent behavior comes increasingly close to the mean field behavior for our computed equilibria as the graph or system size grows, verifying our theory. More generally, we successfully apply policy gradient reinforcement learning in conjunction with sequential Monte Carlo methods.

March 2022 5th Workshop on System Software for Trusted Execution (SysTEX’22) Conference

Always-trusted IoT - Making IoT Devices Trusted with Minimal Overhead

Zsolt István, Paul Rosero, Philippe Bonnet

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Abstract

Internet-of-Things (Iot) devices are becoming increasingly prevalent, with many of them not only relaying data to the Cloud but also being capable of local computation. This capability could be used for many purposes: detecting sensor tampering, compression or anonymization of data before uploading to the cloud, or even participating in distributed Machine Learning. IoT devices are not only at risk of malicious and misbehaving software, but due to their deployment in unprotected locations, they are also at risk of physical attackers and tampering. Even though there are many exciting local computation ideas, the authenticity of computations performed on most IoT devices cannot be guaranteed. In clouds, Trusted Execution Environments (TEEs) already offer trust in the computation carried out even in the presence of a physical attacker, without slowing applications down. In IoT devices, however, such TEEs introduce large performance overheads and increase energy consumption. In this project we propose a radical way forward: to design IoT platforms with processors that do not rely on off-chip memory and instead keep application state on on-chip memory that is easier to protect. This design reduces the overhead of TEEs significantly: it eliminates the cost of securing off-chip memory from attackers. It is important to note that, in addition to fresh thinking on how to design processors with more on-chip memory, computation will also have to be re-imagined to fit in a reduced memory footprint.

February 2022 Other

Nearest-Neighbor-based Collision Avoidance for Quadrotors via Reinforcement Learning

R. Ourari, K. Cui, Ahmed A. Elshamanhory, H. Koeppl

PDF BibTeX DOI: 10.48550/arXiv.2104.14912

February 2022 Cyber Security Politics: Socio-Technological Transformations and Political Fragmentation Other

Cultural Violence and Fragmentation on Social Media: Interventions and Countermeasures by Humans and Social Bots

Jasmin Haunschild, Marc-André Kaufhold, Christian Reuter

BibTeX DOI: 10.4324/9781003110224-5

Abstract

As a prime example of socio-technological transformation, social media services exert an enormous impact on modern culture. They are nowadays widely established for everyday life uses, but also during natural and man-made crises and political conflicts. For instance, Facebook was part of the Arabic Spring, facilitating the communication and interaction between participants of political protests. However, social media is not only used for good: Based on the notions of cultural violence and cultural peace, this chapter shows the potential for political fragmentation through social media, focusing on fake news and terrorism propaganda and their amplified dissemination through social bots. The chapter shows that regarding both the problematic aspects and the countermeasures, technology plays the role of an amplifier, enabling effects such as astroturfing and smoke screening, but also enhancing social bot detection. This chapter examines what these socio-technological transformations through social media imply in terms of legitimacy and trust.

February 2022 Ethik der Digitalstadt - Herausforderung der Datensouveränität Conference

Ethik der Digitalstadt - Herausforderung der Datensouveränität

Martin Schmitt

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February 2022 Computers & Security Article

A Feature-driven Method for Automating the Assessment of OSINT Cyber Threat Sources

Andrea Tundis, Samuel Ruppert, Max Mühlhäuser

PDF BibTeX DOI: 10.1016/j.cose.2021.102576

Abstract

Global malware campaigns and large-scale data breaches show how everyday life can be impacted when the defensive measures fail to protect computer systems from cyber threats. Understanding the threat landscape and the adversaries’ attack tactics to perform it represent key factors for enabling an efficient defense against threats over the time. Of particular importance is the acquisition of timely and accurate information from threats intelligence sources available on the web which can provide additional intelligence on emerging threats even before they can be observed as actual attacks. Currently, specific indicators of compromise (e.g. IP addresses, domains, hashsums of malicious files) are shared in a semi-automated and structured way via so-called threat feeds. Unfortunately, current systems have to deal with the trade-off between the timeliness of such an alert (i.e. warning at the first mention of a threat) and the need to wait for verification by other sources (i.e. warning after multiple sources have verified the threat). In addition, due to the increasing number of open sources, it is challenging to find the right balance between feasibility and costs in order to identify a relatively small subset of valuable sources. In this paper, a method to automate the assessment of cyber threat intelligence sources and predict a relevance score for each source is proposed. Specifically, a model based on meta-data and word embedding is defined and experimented by training regression models to predict the relevance score of sources on Twitter. The results evaluation show that the assigned score allows to reduce the waiting time for intelligence verification, on the basis of its relevance, thus improving the time advantage of early threat detection.

January 2022 JPIM Research Forum 2022 Conference

Hope this E-Mail finds you well – How empathic entrepreneurs succeed during times of adversity

Konstantin Kurz, Carolin Bock, Leonard Hanschur

BibTeX

2022 Universitäts- und Landesbibliothek Darmstadt Darmstadt Other

Spezialbericht - Smart-City-Ranking 2022 - Ressourcenschonend und CO₂-neutral - Die smarte Transformation unserer Städte im Hinblick auf die Energiekrise

Arno Haselhorst, Jürgen Germies, Lucía Wright-Contreras, Luiza Camara, Annette Rudolph-Cleff, Joachim Schulze

PDF BibTeX DOI: 10.26083/tuprints-00022871

Abstract

Welche Möglichkeiten stehen Städten zur Verfügung, um mit Hilfe der Digitalisierung den CO₂-Ausstoß zu reduzieren? Antworten auf diese Frage liefert ein Spezialbericht, welcher in der Zusammenarbeit zwischen Haselhorst Associates GmbH und der Technischen Universität Darmstadt entstanden ist. Unter dem Titel “Ressourcenschonend und CO₂-neutral: Die digitale Transformation unserer Städte im Hinblick auf die Energiekrise” widmet sich dieser Bericht dem Zusammenhang zwischen einer smarten und nachhaltigen Stadtentwicklung und zeigt auf, welche Handlungsmaßnahmen Städte und Stadtwerke angesichts der Energiekrise jetzt ergreifen sollten.

2022 DeSIRE Conference 2022 Conference

Urban data platforms and urban critical infrastructure

Michaela Leštáková, Frank Hessel, Kevin Logan, Yasin Alhamwy, Andreas Morgen, Martin Pietsch

BibTeX

Abstract

Urban data platforms (UDP) are currently being launched in many cities as a part of their smart city strategies. They gather and provide access to data from various urban domains, including critical infrastructure. We performed a survey about UDPs in Germany. Focusing on their potential for improving resilience of the city (resilience through ICT) and the resilience of the UDPs themselves (resilience for ICT), our key findings were: ▪ UDP providers tend to focus on normal conditions rather than crisis ▪ critical infrastructure is often not covered ▪ lack of focus on crisis shows in the design of the UDPs as well

2022 Schmalenbach Journal of Business Research Article

A Friend in Need Is a Friend Indeed? Analysis of the Willingness to Share Self-Produced Electricity During a Long-lasting Power Outage

Konstantin Kurz, Carolin Bock, Michèle Knodt, Anna Stöckl

PDF BibTeX DOI: 10.1007/s41471-022-00148-6

2022 2nd Workshop on Mobile Resilience: Designing Interactive Systems for Crisis Response Conference

Proceedings of the 2nd Workshop on Mobile Resilience: Designing Interactive Systems for Crisis Response

PDF BibTeX DOI: 10.26083/tuprints-00020092

Abstract

Information and communication technologies (ICT), including artificial intelligence, internet of things, and mobile applications can be utilized to tackle important societal challenges, such as the ongoing COVID-19 pandemic. While they may increase societal resilience, their design, functionality, and underlying infrastructures must be resilient against disruptions caused by anthropogenic, natural and hybrid crises, emergencies, and threats. In order to research challenges, designs, and potentials of interactive technologies, this workshop investigated the space of mobile technologies and resilient systems for crisis response, including the application domains of cyber threat and pandemic response.

2022 DeSIRE Conference 2022 Conference

Multi-agent control of fluid systems – comparison of approaches

Kevin Logan, Marius Stürmer, Tim Müller, Tobias Meck, Peter F. Pelz

BibTeX

Abstract

Multi-agent systems allow system-wide, energy efficient control of fluid systems, fulfilling volume flow demands even in the face of disruptions within the communication network.

2022 GLOBECOM 2022 - 2022 IEEE Global Communications Conference Conference

Optimal Offloading Strategies for Edge-Computing via Mean-Field Games and Control

K. Cui, M. B. Yilmaz, A. Tahir, A. Klein, H. Koeppl

PDF BibTeX DOI: 10.1109/GLOBECOM48099.2022.10001412

2022 Technische Universität Darmstadt Darmstadt Thesis

Algorithmisch gestützte Planung dezentraler Fluidsysteme

Tim Moritz Müller

BibTeX

Abstract

Fluidsysteme, wie Kühlkreisläufe oder die Wasserversorgung, sind essenziell für Industrie und Gesellschaft. Aufgrund ihres hohen Energieverbrauchs, ca. 1/3 des weltweiten Strombedarfs, sind jedoch Maßnahmen zur Reduktion der benötigten Eingangsleistung notwendig. Anhand der in dieser Arbeit betrachteten Pumpensysteme zeigen sich zwei Wege, dies zu realisieren: Zum einen kann die benötigte hydraulische Leistung durch dezentrale, verteilte Pumpen gesenkt werden. Zum anderen kann der Systemwirkungsgrad erhöht werden, wofür die Komponenten bestmöglich aufeinander abgestimmt werden müssen. Aus der sich hieraus ergebenden Komplexität folgen bei konventioneller Planung häufig subjektive, intransparente und vor allem suboptimale Entscheidungen. In der Arbeit wird sowohl das Potenzial für Kosten- und Energieeinsparungen durch Dezentralisierung, also auch die algorithmisch gestützte Planung zu Beherrschung der Komplexität behandelt. Dazu wird das Planungsproblem als gemischt-ganzzahliges, nichtlineares Optimierungsproblem formuliert und mittels problemspezifischer, algorithmisch gestützter Methoden gelöst. Als Anwendungsfälle werden Druckerhöhungsanlagen für die Wasserversorgung in Gebäuden sowie ein industrieller Kühlkreislauf betrachtet. Die Ergebnisse werden aus techno-ökonomischer Sicht diskutiert und experimentell validiert. In den Anwendungen können hohe Energie- und Kosteneinsparungen durch Dezentralisierung aufgezeigt werden. Aufgrund der erhöhten Komplexität ist hierzu jedoch eine algorithmisch gestützte Planung notwendig. Es wird zudem gezeigt, dass der Entscheidungsfindungsprozess transparenter gestaltet werden kann, indem Zielkonflikte mittels Pareto-Fronten klar dargelegt werden und energetische Schranken zum Aufzeigen des Potenzials genutzt werden.

2022 Sensors Article

Improving Wearable-Based Activity Recognition Using Image Representations

Alejandro Sanchez Guinea, Mehran Sarabchian, Max Mühlhäuser

PDF BibTeX DOI: 10.3390/s22051840

Abstract

Activity recognition based on inertial sensors is an essential task in mobile and ubiquitous computing. To date, the best performing approaches in this task are based on deep learning models. Although the performance of the approaches has been increasingly improving, a number of issues still remain. Specifically, in this paper we focus on the issue of the dependence of today’s state-of-the-art approaches to complex ad hoc deep learning convolutional neural networks (CNNs), recurrent neural networks (RNNs), or a combination of both, which require specialized knowledge and considerable effort for their construction and optimal tuning. To address this issue, in this paper we propose an approach that automatically transforms the inertial sensors time-series data into images that represent in pixel form patterns found over time, allowing even a simple CNN to outperform complex ad hoc deep learning models that combine RNNs and CNNs for activity recognition. We conducted an extensive evaluation considering seven benchmark datasets that are among the most relevant in activity recognition. Our results demonstrate that our approach is able to outperform the state of the art in all cases, based on image representations that are generated through a process that is easy to implement, modify, and extend further, without the need of developing complex deep learning models.

2022 2022 International Conference on Robotics and Automation (ICRA) Conference

Nearest-Neighbor-based Collision Avoidance for Quadrotors via Reinforcement Learning

Ramzi Ourari, Kai Cui, Ahmed Elshamanhory, Heinz Koeppl

BibTeX DOI: 10.1109/ICRA46639.2022.9812221

2022 35th European Conference on Object-Oriented Programming Conference

Prisma: A tierless language for enforcing contract-client protocols in decentralized apps

David Richter, David Kretzler, Pascal Weisenburger, Guido Salvaneschi, Sebastian Faust, Mira Mezini

PDF BibTeX DOI: 10.4230/LIPIcs.ECOOP.2022.35

Abstract

Decentralized applications (dApps) consist of smart contracts that run on blockchains and clients that model collaborating parties. dApps are used to model financial and legal business functionality. Today, contracts and clients are written as separate programs – in different programming languages – communicating via send and receive operations. This makes distributed program flow awkward to express and reason about, increasing the potential for mismatches in the client-contract interface, which can be exploited by malicious clients, potentially leading to huge financial losses. In this paper, we present Prisma, a language for tierless decentralized applications, where the contract and its clients are defined in one unit and pairs of send and receive actions that “belong together” are encapsulated into a single direct–style operation, which is executed differently by sending and receiving parties. This enables expressing distributed program flow via standard control flow and renders mismatching communication impossible. We prove formally that our compiler preserves program behavior in presence of an attacker controlling the client code. We systematically compare Prisma with mainstream and advanced programming models for dApps and provide empirical evidence for its expressiveness and performance.

2022 30th European Signal Processing Conference Conference

Eigenvalue-Based Block Diagonal Representation and Application to p-Nearest Neighbor Graphs

Aylin Taştan, Michael Muma, Abdelhak M. Zoubir

PDF BibTeX DOI: 10.23919/EUSIPCO55093.2022.9909832

2022 Mensch und Computer 2022: Facing Realities Conference

Detecting a Crisis: Comparison of Self-Reported vs. Automated Internet Outage Measuring Methods

Denis Orlov, Simon Möller, Sven Düfer, Steffen Haesler, Christian Reuter

PDF BibTeX DOI: 10.18420/muc2022-mci-ws10-321

Abstract

Every day, there are internet disruptions or outages around the world that affect our daily lives. In this paper, we analyzed these events in Germany in recent years and found out how they can be detected, and what impact they have on citizens, especially in crisis situations. For this purpose, we take a look at two different approaches to recording internet outages, namely the self-reporting of citizens and automatic reporting by algorithmic examination of the availability of IP networks. We evaluate the data of six major events with regard to their meaningfulness in quality and quantity. We found that due to the amount of data and the inherent imprecision of the methods used, it is difficult to detect outages through algorithmic examination. But once an event is publicly known by self-reporting, they have advantages to capture the temporal and spatial dimensions of the outage due to its nature of objective measurements. As a result, we propose that users’ crowdsourcing can enhance the detection of outages and should be seen as an important starting point to even begin an analysis with algorithm-based techniques, but it is to ISPs and regulatory authorities to support that.

December 2021 IEEE Global Communications Conference 2021 Conference

Joint Beamforming and BS Selection for Energy-Efficient Communications via Aerial-RIS

Jaime Quispe, Tarcisio Ferreira Maciel, Yuri Carvalho Barbosa Silva, Anja Klein

BibTeX DOI: 10.1109/GCWkshps52748.2021.9681981

Abstract

Cooperative BS transmission via unmanned aerial vehicles (UAVs)-airborne reconfigurable intelligent surface (RIS), also known as aerial-RIS, is a promising solution for providing connectivity in emergency areas where network access is unavailable. The RIS requires low power in reflecting the impinging base station (BS) signals towards the direction of the user equipment (UE), and the cooperative transmission can provide a more stable connection that guarantees quality-of-service (QoS). In this work, we investigate the energy efficiency (EE) maximization of a multiple-BS single-UE single-aerial-RIS setup and the usefulness of cooperation to prevent outages. The BSs can be turned either on or off depending on their contribution to the EE, and the system is subject to QoS, power, capacity, and RIS specific constraints. We formulate a problem that jointly optimizes the selection of the BSs and the beamforming weights of BSs and RIS, and solve it with a Branch-Reduce-and-Bound (BRnB) algorithm that uses monotonic optimization and semidefinite relaxation steps. Simulation results for an illustrative setup show that the aerial-RIS increases the EE by 50% when doubling the number of its elements and cooperative aerial-RIS transmissions help to solve outages of single-BS cases.

December 2021 IEEE Global Communications Conference 2021 Conference

Beamforming and link activation methods for energy efficient RIS-aided transmissions in C-RANs

Jaime Quispe, Tarcisio Ferreira Maciel, Yuri Carvalho Barbosa Silva, Anja Klein

BibTeX DOI: 10.1109/GLOBECOM46510.2021.9685593

Abstract

This work studies the application of a reconfigurable intelligent surface (RIS) in a cloud radio access network (C-RAN) targeting the reduction of resource usage while providing adequate capacity. We investigate if an RIS can contribute to improve the trade-off between the downlink system spectral efficiency (SE) and energy consumption of a multi-base-station (BS) multi-user single-RIS setup by means of link activation, radiated power control, and operational power mode decisions that can benefit from RIS-enhanced radio channels. For this purpose, we optimize the activations jointly with BS and RIS beamforming for maximum energy efficiency (EE) under a centralized approach and subject to SE, power, fronthaul capacity, and RIS phase-shift constraints. The associated mixed-boolean non-linear problem is solved using monotonic and semidefinite relaxation methods integrated in a Branch-Reduce-and-Bound procedure. Simulations show that the RIS helps to increase the EE of a C-RAN w.r.t. its non-RIS-aided and fully-connected versions by 30% and 80%, respectively.

December 2021 94th Vehicular Technology Conference (VTC2021-Fall) Conference

Energy-Optimal Short Packet Transmission for Time-Critical Control

Kilian Kiekenap, Andrea Patricia Ortiz Jimenez, Anja Klein

BibTeX DOI: 10.1109/VTC2021-Fall52928.2021.9625205

Abstract

In this paper, the transmission energy for reliable communications with short packets and low latency requirements, e.g. for control applications, is minimized. Since the dynamics of the agents determine the allowed latencies for receiving control inputs, the requirements on latency and allowable packet error rate are individual, depending on the machine type. We consider a centralized environment with a single controller transmitting control commands wireless to multiple agents with given latency requirements. Also, the channel conditions are individual for each agent. Therefore, the optimal time-frequency resource allocation is derived for continuous time-frequency resource allocation. Since the resource allocation in OFDM systems like 5G is discrete, an algorithm to select the allocation from a resource grid with different resolutions is proposed and shown to achieve solutions with less than 0.5 dB increase in energy consumption compared to the continuous results. With numerical evaluation, the benefit of a channel-state- and deadline-aware solution is shown for a resource grid based on the 5G frame structure. On average, the gain of the proposed algorithm to an allocation only balancing the number of resources for each agent, as far as the deadlines allow, is about 50% energy saving.

December 2021 94th Vehicular Technology Conference (VTC2021-Fall) Conference

Energy Consumption Optimization for UAV Assisted Private Blockchain-based IIoT Networks

Xinhua Lin, Jing Zhang, Lin Xiang, Xiohu Ge

BibTeX DOI: 10.1109/VTC2021-Fall52928.2021.9625316

Abstract

The blockchain is a promising technology to enhance the security and resilience of industrial Internet of Things (IIoT) networks. However, generating blockchain for the IIoT devices usually consumes excessive energy which may not be affordable for battery-powered IIoT devices. To address this problem, in this paper, we consider an unmanned aerial vehicle (UAV) assisted private blockchain-based IIoT system. Thereby, a UAV mounted with computing processor is deployed as a multi-access edge computing platform, which is responsible for collecting data from the IIoT devices, generating blocks based on the collected data, and broadcasting the blocks to the IIoT devices. To minimize the energy consumption of the UAV, joint optimization of the central processing unit (CPU) frequencies for data computation and block generation, the amount of offloaded IIoT data, the bandwidth allocation, and the trajectory of the UAV is formulated as a nonconvex optimization problem and solved via a successive convex approximation (SCA) algorithm. Simulation results show that, compared with several baseline schemes, the proposed scheme can significantly lower the energy consumption required for the blockchain generation in IIoT networks.

December 2021 29th European Signal Processing Conference (EUSIPCO) Conference

Robust Spectral Clustering: A Locality Preserving Feature Mapping Based on M-estimation

A. Taştan, M. Muma, A. M. Zoubir

BibTeX DOI: 10.23919/EUSIPCO54536.2021.9616292

Abstract

Dimension reduction is a fundamental task in spectral clustering. In practical applications, the data may be corrupted by outliers and noise, which can obscure the underlying data structure. The effect is that the embeddings no longer represent the true cluster structure. We therefore propose a new robust spectral clustering algorithm that maps each high-dimensional feature vector onto a low-dimensional vector space. Robustness is achieved by posing the locality preserving feature mapping problem in form of a ridge regression task that is solved with a penalized M-estimation approach. An unsupervised penalty parameter selection strategy is proposed using the Fiedler vector, which is the eigenvector associated with the second smallest eigenvalue of a connected graph. More precisely, the penalty parameter is selected, such that, the corresponding Fiedler vector is Δ-separated with a minimum information loss on the embeddings. The method is benchmarked against popular embedding and spectral clustering approaches using real-world datasets that are corrupted by outliers.

December 2021 Computers & Electrical Engineering Article

An IoT-based context-aware model for danger situations detection

Andrea Tundis, Muhammad Uzair, Max Mühlhäuser

PDF BibTeX DOI: 10.1016/j.compeleceng.2021.107571

Abstract

On a daily basis, people perform planned or routine activities related to their needs, such as going to the office, playing sports and so on. Alongside them, unpleasant unforeseen situations can take place such as being robbed on the street or even being taken hostage. Providing information related to the crime scene or requesting help from the competent authorities is difficult. That is why, mechanisms to support users in such situations, based on their physical status, would be of great importance. Based on such idea, a context-aware model for detecting specific situations of danger is proposed. It is characterized by a set of defined features related to the body posture, the stress level and geolocation whose values are gathered through a smartphone and a smartwatch, as enabling technologies for the local computation. A machine learning technique was adopted for supporting body posture recognition, whereas a threshold-based approach was used to detect the stress level and to evaluate of userâ��s location. After the description of the proposed model, the achieved results as well as current limits are also discussed.

December 2021 Computers & Electrical Engineering Article

A social media-based over layer on the edge for handling emergency-related events

Andrea Tundis, Maksim Melnik, Hashim Naveed, Max Mühlhäuser

PDF BibTeX DOI: 10.1016/j.compeleceng.2021.107570

Abstract

Online Social Networks (OSNs), together with messaging services are tools for the exchange of entertainment-related information. However, they represent virtual environments capable of providing relevant information related to emergency or criminal events. Thanks to the simple way of using OSNs in combination to modern ubiquitous Internet of Things (IoT) smart devices, the generation and exploitation of such information is made available to users in real-time even more easily. Unfortunately, its reuse has not been taken into consideration yet due to the lack of specific models and related software tools. In this context, the paper presents a social media-based over layer for supporting the monitoring, detection, computation and information sharing of social media information related to emergency scenarios centered on smartphones and text mining techniques. The proposal is assessed through two different case studies, by evaluating the performances of different classifiers and by showing the logic of the functionalities of the related apps.

2021 11th IEEE Global Humanitarian Technology Conference (GHTC 2021) Conference

coreemu-lab: An Automated Network Emulation and Evaluation Environment

Lars Baumgärtner, Tobias Meuser, Bastian Bloessl

BibTeX DOI: 10.1109/GHTC53159.2021.9612475

November 2021 11th IEEE Global Humanitarian Technology Conference (GHTC 2021) Conference

NetSkylines: Digital Twins for Evaluating Disaster Communication

Tobias Meuser, Lars Baumgärtner, Benjamin Becker

PDF BibTeX DOI: 10.1109/GHTC53159.2021.9612413

November 2021 Journal of Contingencies and Crisis Management (JCCM) Article

Local governance of critical infrastructure resilience: A survey of leadership, planning, and positive coordination in German cities

Cornelia Fraune, Alice Engel, Michèle Knodt

PDF BibTeX DOI: 10.1111/1468-5973.12386

November 2021 19th ACM International Symposium on Mobility Management and Wireless Access (MobiWac’21) Conference

Desynchronization and MitM Attacks Against Neighbor Awareness Networking Using OpenNAN

Lars Almon, Arno Krause, Oliver Fietze, Matthias Hollick

PDF BibTeX DOI: 10.1145/3479241.3486689

November 2021 Transactions on Cryptographic Hardware and Embedded Systems Article

Can’t Touch This: Inertial HSMs Thwart Advanced Physical Attacks

Jan Sebastian Götte, Björn Scheuermann

PDF BibTeX DOI: 10.46586/tches.v2022.i1.69-93

Abstract

In this paper, we introduce a novel countermeasure against physical attacks: Inertial Hardware Security Modules (IHSMs). Conventional systems have in common that their security requires the crafting of fine sensor structures that respond to minute manipulations of the monitored security boundary or volume. Our approach is novel in that we reduce the sensitivity requirement of security meshes and other sensors and increase the complexity of any manipulations by rotating the security mesh or sensor at high speed—thereby presenting a moving target to an attacker. Attempts to stop the rotation are easily monitored with commercial MEMS accelerometers and gyroscopes. Our approach leads to an HSM that can easily be built from off-the-shelf parts by any university electronics lab, yet offers a level of security that is comparable to commercial HSMs. We have built a proof-of-concept hardware prototype that demonstrates solutions to the concept’s main engineering challenges. As part of this proof-of-concept, we have found that a system using a coarse security mesh made from commercial printed circuit boards and an automotive high-g-force accelerometer already provides a useful level of security.

November 2021 17th International Conference on distributed Computing in sensor systems (DCOSS 2021) Conference

Leveraging Cross-Technology Broadcast Communication to build Gateway-Free Smart Homes

Hannah Brunner, Rainer Hofmann, Markus Schuss, Jakob Link, Matthias Hollick, Carlo Alberto Boano, Kay Römer

BibTeX DOI: 10.1109/DCOSS52077.2021.00014

November 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021) Conference

MAD-G: Multilingual Adapter Generation for Efficient Cross-Lingual Transfer

Alan Ansell, Maria Ponti Edoardo, Jonas Pfeiffer, Sebastian Ruder, Goran Glavaš, Ivan Vulić, Anna Korhonen

PDF BibTeX

Abstract

Adapter modules have emerged as a general parameter-efficient means to specialize a pretrained encoder to new domains. Massively multilingual transformers (MMTs) have particularly benefited from additional training of language-specific adapters. However, this approach is not viable for the vast majority of languages, due to limitations in their corpus size or compute budgets. In this work, we propose MAD-G (Multilingual ADapter Generation), which contextually generates language adapters from language representations based on typological features. In contrast to prior work, our time- and space-efficient MAD-G approach enables (1) sharing of linguistic knowledge across languages and (2) zero-shot inference by generating language adapters for unseen languages. We thoroughly evaluate MAD-G in zero-shot cross-lingual transfer on part-of-speech tagging, dependency parsing, and named entity recognition. While offering (1) improved fine-tuning efficiency (by a factor of around 50 in our experiments), (2) a smaller parameter budget, and (3) increased language coverage, MAD-G remains competitive with more expensive methods for language-specific adapter training across the board. Moreover, it offers substantial benefits for low-resource languages, particularly on the NER task in low-resource African languages. Finally, we demonstrate that MAD-G’s transfer performance can be further improved via: (i) multi-source training, i.e., by generating and combining adapters of multiple languages with available task-specific training data; and (ii) by further fine-tuning generated MAD-G adapters for languages with monolingual data.

October 2021 Jahrestagung der Gesellschaft für Informatik (INFORMATIK 2021) Conference

Energy-efficient Mobile Sensor Data Offloading via WiFi using LoRa-based Connectivity Estimations

Julian Zobel, Paul Frommelt, Patrick Lieser, Jonas Höchst, Patrick Lampe, Bernd Freisleben, Ralf Steinmetz

BibTeX DOI: 10.18420/informatik2021-037

Abstract

Animal monitoring in natural habitats provides significant insights into the animals’ behavior, interactions, health, or external influences. However, the sizes of monitoring devices attachable to animals strongly depends on the animals’ sizes, and thus the range of possible sensors including batteries is severely limited. Gathered data can be offloaded from monitoring devices to data sinks in a wireless sensor network using available radio access technologies, but this process also needs to be as energy-efficient as possible. This paper presents an approach to combine the benefits of high-throughput WiFi and robust low-power LoRa communication for energy-efficient data offloading. WiFi is only used when connectivity between mobile devices and data sinks is available, which is determined by LoRa-based distance estimations without the need for additional GPS sensors. A prototypical implementation on low-end commodity-off-the-shelf hardware is used to evaluate the proposed approach in a German mixed forest using a simple path loss model for distance estimation. The system provides an offloading success rate of 87%, which is similar to that of a GPS-based approach, but with around 37% less power consumption.

October 2021 2021 Conference on Computer Supported Cooperative Work and Social Computing Conference

Connected Self-Organized Citizens in Crises: An Interdisciplinary Resilience Concept for Neighborhoods

Steffen Haesler, Ragnar Mogk, Florentin Putz, Kevin Logan, Nadja Thiessen, Katharina Kleinschnitger, Lars Baumgärtner, Jan-Philipp Stroscher, Christian Reuter, Michèle Knodt, Matthias Hollick

PDF BibTeX DOI: 10.1145/3462204.3481749

October 2021 5th European Conference on Mobile Robots Conference

Industrial Manometer Detection and Reading for Autonomous Inspection Robots

Jonas Günther, Martin Oehler, Stefan Kohlbrecher, Oskar von Stryk

BibTeX DOI: 10.1109/ECMR50962.2021.9568833

Abstract

Autonomous mobile robots for industrial inspection can reduce cost for digitalization of existing plants by performing autonomous routine inspections. A frequent task is reading of analog gauges to monitor the health of the facility. Automating this process involves capturing image data with a camera sensor and processing the data to read the value. Detection algorithms deployed on a mobile robot have to deal with increased uncertainty regarding localization and environmental influences. This imposes increased requirements regarding robustness to viewing angle, lighting and scale variation on detection and reading. Current approaches based on conventional computer vision require high quality images or prior knowledge. We address these limitations by leveraging the advances of neural networks in the task of object detection and instance segmentation in a two-stage pipeline. Our method robustly detects and reads manometers without prior knowledge of object location or exact object type. In our evaluation we show that our approach can detect and read manometers from a distance of up to 3 m and a viewing angle of up to 60° in different lighting conditions with needle angle estimation errors of ±2.2°. We publish the validation split of our training dataset for manometer and needle detection at https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2881.

October 2021 5th European Conference on Mobile Robots Conference

Open-Source Tools for Efficient ROS and ROS2-based 2D Human-Robot Interface Development

Stefan Fabian, Oskar von Stryk

BibTeX DOI: 10.1109/ECMR50962.2021.9568801

Abstract

2D human-robot interfaces (HRI) are a key component of most robotic systems with an (optional) teleoperation component. However, creating such an interface is often cumbersome and time-consuming since most user interface frameworks require recompilation on each change or the writing of extensive boilerplate code even for simple interfaces. In this paper, we introduce five open-source packages, namely, the ros(2)babelfish packages, the qmlros(2)plugin packages, and the hectorrvizoverlay package. These packages enable the creation of visually appealing end-user or functionality-oriented diagnostic interfaces for ROS- and ROS2-based robots in a simple and quick fashion using the QtWidget or QML user interface framework. Optionally, rendering the interface as an overlay of the 3D scene of the robotics visualization tool rviz enables developers to leverage existing extensive data visualization capabilities.

October 2021 5th European Conference on Mobile Robots Conference

A Flexible Framework for Virtual Omnidirectional Vision to Improve Operator Situation Awareness

Martin Oehler, Oskar von Stryk

BibTeX DOI: 10.1109/ECMR50962.2021.9568840

Abstract

During teleoperation of a mobile robot, providing good operator situation awareness is a major concern as a single mistake can lead to mission failure. Camera streams are widely used for teleoperation but offer limited field-of-view. In this paper, we present a flexible framework for virtual projections to increase situation awareness based on a novel method to fuse multiple cameras mounted anywhere on the robot. Moreover, we propose a complementary approach to improve scene understanding by fusing camera images and geometric 3D Lidar data to obtain a colorized point cloud. The implementation on a compact omnidirectional camera reduces system complexity considerably and solves multiple use-cases on a much smaller footprint compared to traditional approaches such as actuated pan-tilt units. Finally, we demonstrate the generality of the approach by application to the multi-camera system of the Boston Dynamics Spot. The software implementation is available as open-source ROS packages on the project page https://tu-darmstadt-ros-pkg.github.io/omnidirectionalvision.

October 2021 11th IEEE Global Humanitarian Technology Conference (GHTC 2021) Conference

Calling Ground Support: Cooperative DTNs for Improved Aerial Monitoring Systems

Julian Zobel, Tobias Meuser, Ralf Steinmetz

BibTeX

October 2021 Proceedings of the ACM on Human Computer Interaction Article

Stronger Together: How Neighborhood Groups Build up a Virtual Network during the COVID-19 Pandemic

Steffen Haesler, Stefka Schmid, Annemike Sophia Vierneisel, Christian Reuter

PDF BibTeX DOI: 10.1145/3476045

October 2021 Other

The Terminating-Knockoff Filter: Fast High-Dimensional Variable Selection with False Discovery Rate Control

J. Machkour, M. Muma, D. P. Palomar

PDF BibTeX DOI: 10.48550/arXiv.2110.06048

Abstract

We propose the Terminating-Knockoff (T-Knock) filter, a fast variable selection method for high-dimensional data. The T-Knock filter controls a user-defined target false discovery rate (FDR) while maximizing the number of selected true positives. This is achieved by fusing the solutions of multiple early terminated random experiments. The experiments are conducted on a combination of the original data and multiple sets of randomly generated knockoff variables. A finite sample proof based on martingale theory for the FDR control property is provided. Numerical simulations show that the FDR is controlled at the target level while allowing for a high power. We prove under mild conditions that the knockoffs can be sampled from any univariate distribution. The computational complexity of the proposed method is derived and it is demonstrated via numerical simulations that the sequential computation time is multiple orders of magnitude lower than that of the strongest benchmark methods in sparse high-dimensional settings. The T-Knock filter outperforms state-of-the-art methods for FDR control on a simulated genome-wide association study (GWAS), while its computation time is more than two orders of magnitude lower than that of the strongest benchmark methods.

October 2021 4th International Workshop on Emerging Technologies for Authorization and Authentication Conference

Future-Proof Web Authentication: Bring Your Own FIDO2 Extensions

Florentin Putz, Steffen Schön, Matthias Hollick

PDF BibTeX DOI: 10.1007/978-3-030-93747-8_2

Abstract

The FIDO2 standards for strong authentication on the Internet define an extension interface, which allows them to flexibly adapt to future use cases. The domain of establishing new FIDO2 extensions, however, is currently limited to web browser developers and members of the FIDO alliance. We show how researchers and developers can design and implement their own extensions for using FIDO2 as a well-established and secure foundation to demonstrate innovative authentication concepts or to support custom deployments. Our open-source implementation targets the full FIDO2 stack, such as the Chromium web browser and hardware tokens, to enable tailor-made authentication based on the power of the existing FIDO2 ecosystem. To give an overview of existing extensions, we survey all published FIDO2 extensions by manually inspecting the source code of major web browsers and authenticators. Their current design, however, hinders the implementation of custom extensions, and they only support a limited number of extensions out of the box. We discuss weaknesses of current implementations and identify the lack of extension pass-through as a major limitation in current FIDO2 clients.

October 2021 Book

Mastering Uncertainty in Mechanical Engineering

P. F. Pelz, Peter Groche, Marc E. Pfetsch, Maximilian Frederic Schäffner

PDF BibTeX DOI: 10.1007/978-3-030-78354-9

Abstract

This open access book reports on innovative methods, technologies and strategies for mastering uncertainty in technical systems. Despite the fact that current research on uncertainty is mainly focusing on uncertainty quantification and analysis, this book gives emphasis to innovative ways to master uncertainty in engineering design, production and product usage alike. It gathers authoritative contributions by more than 30 scientists reporting on years of research in the areas of engineering, applied mathematics and law, thus offering a timely, comprehensive and multidisciplinary account of theories and methods for quantifying data, model and structural uncertainty, and of fundamental strategies for mastering uncertainty. It covers key concepts such as robustness, flexibility and resilience in detail. All the described methods, technologies and strategies have been validated with the help of three technical systems, i.e. the Modular Active Spring-Damper System, the Active Air Spring and the 3D Servo Press, which have been in turn developed and tested during more than ten years of cooperative research. Overall, this book offers a timely, practice-oriented reference guide to graduate students, researchers and professionals dealing with uncertainty in the broad field of mechanical engineering.

October 2021 15th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM ’21) Conference

Python Crypto Misuses in the Wild

Anna-Katharina Wickert, Lars Baumgärtner, Florian Breitfelder, Mira Mezini

PDF BibTeX DOI: 10.6084/m9.figshare.16499085.v1

October 2021 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing Conference

Bridging from Crisis to Everyday Life – An Analysis of User Reviews of the Warning App NINA and the COVID-19 Information Apps CoroBuddy and DarfIchDas

Jasmin Haunschild, Christian Reuter

BibTeX DOI: 10.1145/3462204.3481745

October 2021 Signal Processing Article

Sparsity-aware Robust Community Detection (SPARCODE)

A. Taştan, M. Muma, A. M. Zoubir

PDF BibTeX DOI: 10.1016/j.sigpro.2021.108147

September 2021 INFORMATIK 2021: 51. Jahrestagung der Gesellschaft für Informatik Conference

Technical & Data Protection Aspects of a Smart Digital Control Center for Smart Cities

Jan-Philipp Stroscher, Marius Schnaubelt, Alejandro Sanchez Guinea, Stefan Fabian, Julius von Willich, Yasin Alhamwy, Maximilian Bauer, Oskar von Stryk, Max Mühlhäuser, Kurt Geihs, Gerrit Hornung

PDF BibTeX DOI: 10.18420/informatik2021-086

September 2021 UbiComp ‘21: The 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing Conference

VIDENS: Vision-based User Identification from Inertial Sensors

Alejandro Sanchez Guinea, Simon Heinrich, Max Mühlhäuser

BibTeX DOI: 10.1145/3460421.3480426

September 2021 43rd DAGM German Conference on Pattern Recognition 2021 Conference

TxT: Crossmodal End-to-End Learning with Transformers

Jan-Martin O. Steitz, Jonas Pfeiffer, Iryna Gurevych, Stefan Roth

PDF BibTeX DOI: 10.1007/978-3-030-92659-5_26

Abstract

Reasoning over multiple modalities, e.g. in Visual Question Answering (VQA), requires an alignment of semantic concepts across domains. Despite the widespread success of end-to-end learning, today’s multimodal pipelines by and large leverage pre-extracted, fixed features from object detectors, typically Faster R-CNN, as representations of the visual world. The obvious downside is that the visual representation is not specifically tuned to the multimodal task at hand. At the same time, while transformer-based object detectors have gained popularity, they have not been employed in today’s multimodal pipelines. We address both shortcomings with TxT, a transformer-based crossmodal pipeline that enables fine-tuning both language and visual components on the downstream task in a fully end-to-end manner. We overcome existing limitations of transformer-based detectors for multimodal reasoning regarding the integration of global context and their scalability. Our transformer-based multimodal model achieves considerable gains from end-to-end learning for multimodal question answering.

September 2021 NetSys Conference

Information Management for Multi-Agent Systems

Yasin Alhamwy

PDF BibTeX

September 2021 2021 Conference on Empirical Methods in Natural Language Processing Conference

Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation

Leonardo F. R. Ribeiro, Jonas Pfeiffer, Yue Zhang, Iryna Gurevych

PDF BibTeX

September 2021 ACM International Conference on Information Technology for Social Good Conference

Citizens‘ Perceived Information Responsibilities and Information Challenges During the COVID-19 Pandemic

Christian Reuter, Selina Pauli, Jasmin Haunschild

BibTeX DOI: 10.1145/3462203.3475886

September 2021 i-com: Journal of Interactive Media Article

Towards Resilient Critical Infrastructures - Motivating Users to Contribute to Smart Grid Resilience

Rolf Egert, Nina Gerber, Jasmin Haunschild, Philipp Kuehn, Verena Zimmermann

BibTeX DOI: 10.1515/icom-2021-0021

Abstract

Smart cities aim at improving efficiency while providing safety and security by merging conventional infrastructures with information and communication technology. One strategy for mitigating hazardous situations and improving the overall resilience of the system is to involve citizens. For instance, smart grids involve prosumers—capable of producing and consuming electricity—who can adjust their electricity profile dynamically (i. e., decrease or increase electricity consumption), or use their local production to supply electricity to the grid. This mitigates the impact of peak consumption periods on the grid and makes it easier for operators to control the grid. This involvement of prosumers is accompanied by numerous socio-technical challenges, including motivating citizens to contribute by adjusting their electricity consumption to the requirements of the energy grid. Towards this end, this work investigates motivational strategies and tools, including nudging, persuasive technologies, and incentives, that can be leveraged to increase the motivation of citizens. We discuss long-term and side effects and ethical and privacy considerations, before portraying bug bounty programs, gamification and apps as technologies and strategies to communicate the motivational strategies to citizens.

September 2021 Conference on Information Technology for Social Good Conference

Citizens’ Perceived Information Responsibilities and Information Challenges During the COVID-19 Pandemic

Jasmin Haunschild, Selina Pauli, Christian Reuter

BibTeX DOI: 10.1145/3462203.3475886

September 2021 International Conference on Networked Systems (NetSys2021) Conference

Dynamic Monitoring Area Allocation for Aerial Post-Disaster Situation Monitoring

Julian Zobel, Niklas Stöhr, Ralf Kundel, Patrick Lieser, Ralf Steinmetz

BibTeX

August 2021 The 2021 Conference on Empirical Methods in Natural Language Processing Conference

AdapterDrop: On the Efficiency of Adapters in Transformers

Andreas Rücklé, Gregor Geigle, Max Glockner, Tilman Beck, Jonas Pfeiffer, Nils Reimers, Iryna Gurevych

PDF BibTeX

August 2021 The 2021 Conference on Empirical Methods in Natural Language Processing Conference

What to Pre-Train on? Efficient Intermediate Task Selection

Clifton Poth, Jonas Pfeiffer, Andreas Rücklé, Iryna Gurevych

PDF BibTeX

August 2021 The 2021 Conference on Empirical Methods in Natural Language Processing Conference

UNKs Everywhere: Adapting Multilingual Language Models to New Scripts

Jonas Pfeiffer, Ivan Vulić, Iryna Gurevych, Sebastian Ruder

PDF BibTeX

August 2021 Water Article

A Method for Modeling Urban Water Infrastructures Combining Geo-Referenced Data

Imke-Sophie Rehm, John Friesen, Kevin Pouls, Christoph Busch, Hannes Taubenböck, Peter F. Pelz

PDF BibTeX DOI: 10.3390/w13162299

Abstract

Water distribution networks are the backbone of any municipal water supply. Their task is to supply the population regardless of the respective demand. High resilience of these infrastructures is of great importance and has brought these infrastructures into the focus of science and politics. At the same time, the data collected is highly sensitive and often openly unavailable. Therefore, researchers have to rely on models that represent the topology of these infrastructures. In this work, a model is developed that allows the topology of an urban water infrastructure to be mapped using the example of Cologne, Germany by combining freely available data. On the one hand, spatial data on land use (local climate zones) are used to disaggregate the water demand within the city under consideration. On the other hand, the parallelism of water and urban transportation infrastructures is used to identify the topology of a network by applying optimization methods. These networks can be analyzed to identify vulnerable areas within urban structures.

August 2021 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering Conference

Automating serverless deployments for DevOps organizations

Daniel Sokolowski, Pascal Weisenburger, Guido Salvaneschi

BibTeX DOI: 10.1145/3468264.3468575

August 2021 HMD : Praxis der Wirtschaftsinformatik Article

Nutzer, Sammler, Entscheidungsträger? Arten der Bürgerbeteiligung in Smart Cities

Jasmin Haunschild, Kilian Demuth, Henri-Jacques Geiß, Christian Richter, Christian Reuter

BibTeX DOI: 10.1365/s40702-021-00770-8

August 2021 Zeitschrift für das gesamte Sicherheitsrecht (GSZ) Article

Datenschutz in der Katastrophe

Gerrit Hornung, Jan-Philipp Stroscher

BibTeX

August 2021 30th USENIX Security Symposium Conference

PrivateDrop: Practical Privacy-Preserving Authentication for Apple AirDrop

Alexander Heinrich, Matthias Hollick, Thomas Schneider, Milan Stute, Christian Weinert

PDF BibTeX

Abstract

Apple’s offline file-sharing service AirDrop is integrated into more than 1.5 billion end-user devices worldwide. We discovered two design flaws in the underlying protocol that allow attackers to learn the phone numbers and email addresses of both sender and receiver devices. As a remediation, we study the applicability of private set intersection (PSI) to mutual authentication, which is similar to contact discovery in mobile messengers. We propose a novel optimized PSI-based protocol called PrivateDrop that addresses the specific challenges of offline resource-constrained operation and integrates seamlessly into the current AirDrop protocol stack. Using our native PrivateDrop implementation for iOS and macOS, we experimentally demonstrate that PrivateDrop preserves AirDrop’s exemplary user experience with an authentication delay well below one second. We responsibly disclosed our findings to Apple and open-sourced our PrivateDrop implementation.

July 2021 Water Article

Water Distribution in a Socio-Technical System: Resilience Assessment for Critical Events Causing Demand Relocation

Kevin Logan, Michaela Lestakova, Nadja Thiessen, Jens Ivo Engels, Peter F. Pelz

PDF BibTeX DOI: 10.3390/w13152062

July 2021 14th IEEE PowerTech Conference Conference

Robust voltage regulation for active distribution networks with imperfect observability

Edwin Mora, Florian Steinke

PDF BibTeX DOI: 10.1109/PowerTech46648.2021.9495084

July 2021 Universität Kassel Kassel Thesis

A Self-Organising Multi-Agent Knowledge Base

Stefan Jakob

BibTeX

July 2021 Other

Krisenfest durch dunkle Zeiten - Wie resilient sind deutsche Großstädte gegenüber Stromausfällen?

Alice Knauf, Michèle Knodt

PDF BibTeX DOI: 10.5281/zenodo.5082350

Abstract

Das System unserer kritischen Infrastrukturen wird komplexer und krisenanfälliger. Menschliches oder technisches Versagen, Naturkatastrophen, Pandemien, Cyber- oder Terrorangriffe können auch in Deutschland zu einem überregionalen Stromausfall führen, der länger als 24 Stunden anhält. Städte stehen dann als untere Katastrophenschutzbehörden vor der großen Herausforderung auf dieses Szenario zu reagieren und bis zu seiner Bewältigung möglichst gut durch die Krise zu kommen. An der Technischen Universität Darmstadt wurden im Rahmen von emergenCITY die Maßnahmen der lokalen Katastrophenschutzämter deutscher kreisfreier Großstädte auf das Szenario untersucht. Es zeigt sich, dass sich die meisten Katastrophenschutzämter mit dem Szenario auseinandersetzen. Dabei stehen interne Vorbereitungen im Bereich der Ressourcenausstattung im Vordergrund. Die Zusammenarbeit des Katastrophenschutzamtes beschränkt sich jedoch in vielen Städten auf einen einmaligen Austausch mit wenigen weiteren lokalen Akteuren. Um zukünftig gegenüber dem Szenario besser gewappnet zu sein, zeigen wir sechs Handlungsoptionen für häufig auftretende Problemfelder auf: Den Umgang mit dem Szenario üben Auf eine angespannte Personalsituation einstellen Katastrophenschutzamt personell stärken Bevölkerung in ihrer Vielfalt wahrnehmen Katastrophenschutz als Querschnittsaufgabe stärken Kooperative Formate verstetigen und ausbauen

July 2021 IEEE Transactions on Vehicular Technology Article

Robust Energy-Efficient Transmission for Wireless-Powered D2D Communication Networks

Jing Zhang, Yanxia Zhang, Lin Xiang, Yan Sun, Derrick Wing Kwan Ng, Minho Jo

PDF BibTeX DOI: 10.1109/TVT.2021.3095626

June 2021 14th ACM Conference on Security and Privacy in Wireless and Mobile Networks Conference

AirCollect: Efficiently Recovering Hashed Phone Numbers Leaked via Apple AirDrop

Alexander Heinrich, Matthias Hollick, Thomas Schneider, Milan Stute, Christian Weinert

PDF BibTeX DOI: 10.1145/3448300.3468252

Abstract

Apple’s file-sharing service AirDrop leaks phone numbers and email addresses by exchanging vulnerable hash values of the user’s own contact identifiers during the authentication handshake with nearby devices. In a paper presented at USENIX Security’21, we theoretically describe two attacks to exploit these vulnerabilities and propose “PrivateDrop” as a privacy-preserving drop-in replacement for Apple’s AirDrop protocol based on private set intersection. In this demo, we show how these vulnerabilities are efficiently exploitable via Wi-Fi and physical proximity to a target. Privacy and security implications include the possibility of conducting advanced spear phishing attacks or deploying multiple “collector” devices in order to build databases that map contact identifiers to specific locations. For our proof-of-concept, we leverage a custom rainbow table construction to reverse SHA-256 hashes of phone numbers in a matter of milliseconds. We discuss the trade-off between success rate and storage requirements of the rainbow table and, after following responsible disclosure with Apple, we publish our proof-of-concept implementation as “AirCollect” on GitHub.

June 2021 14th ACM Conference on Security and Privacy in Wireless and Mobile Networks Conference

OpenHaystack: A Framework for Tracking Personal Bluetooth Devices via Apple’s Massive Find My Network

Alexander Heinrich, Milan Stute, Matthias Hollick

BibTeX DOI: 10.1145/3448300.3468251

Abstract

OpenHaystack is an open-source framework for locating personal Bluetooth devices using Apple’s Find My Network. A user can integrate it into Bluetooth-capable devices, such as notebooks, or create custom tracking accessories that can be attached to personal items (key rings, backpacks, etc.). We provide firmware images for the Nordic nRF5 chips and the ESP32. We show that they consume little energy and run from a single coin cell for a year. Our macOS application can locate personal accessories. Finally, we make both application and firmware available on GitHub.

June 2021 IEEE Transactions on Signal Processing Article

Real Elliptically Skewed Distributions and Their Application to Robust Cluster Analysis

C. A. Schroth, M. Muma

BibTeX DOI: 10.1109/TSP.2021.3092373

June 2021 1st European Workshop on Maritime Systems Resilience and Security (MARESEC 2021) Conference

A Feasibility Analysis of Tether Localisation for Mobile Robots

Yasin Alhamwy, Simon Watson, Joaquin Carrasco, Glen Cooper, Kurt Geihs

BibTeX

June 2021 Book

Crowdfunding in Zeiten von COVID-19: Ein geeignetes Finanzinstrument in der Krise?

Carolin Bock, Konstantin Kurz, Iliyana Madina, Julia Bejan, Robin Bürger, Jens Rockel

BibTeX DOI: 10.24406/publica-fhg-301034

June 2021 Other

Krisenfest durch dunkle Zeiten: Wie resilient sind deutsche Großsstädte gegenüber Stromausfällen?

Michèle Knodt, Alice Knauf

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May 2021 4th International Conference on Uncertainty in Mechanical Engineering Conference

Comparability of Water Infrastructure Resilience of Different Urban Structures

Imke Lorenz, Kevin Pouls, Peter F. Pelz

BibTeX DOI: 10.1007/978-3-030-77256-7_8

May 2021 18th International Conference on Information Systems for Crisis Response and Management (ISCRAM) Conference

Pandemic Skylines: Digital Twins for More Realism in Epidemic Simulations

Tobias Meuser, Lars Baumgärtner, Patrick Lieser

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May 2021 Technische Universität Darmstadt Wiesbaden Book

Information Refinement Technologies for Crisis Informatics: User Expectations and Design Implications for Social Media and Mobile Apps

Marc-André Kaufhold

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Abstract

Marc-André Kaufhold explores user expectations and design implications for the utilization of new media in crisis management and response. He develops a novel framework for information refinement, which integrates the event, organisational, societal, and technological perspectives of crises. Therefore, he reviews the state of the art on crisis informatics and empirically examines the use, potentials and barriers of both social media and mobile apps. Based on these insights, he designs and evaluates ICT concepts and artifacts with the aim to overcome the issues of information overload and quality in large-scale crises, concluding with practical and theoretical implications for technology adaptation and design.

May 2021 Book

Sicherheitskritische Mensch-Computer-Interaktion : Interaktive Technologien und Soziale Medien im Krisen- und Sicherheitsmanagement

PDF BibTeX DOI: 10.1007/978-3-658-32795-8

Abstract

Die zweite, aktualisierte Auflage dieses Lehr- und Fachbuchs gibt eine fundierte und praxisbezogene Einführung sowie einen Überblick über Grundlagen, Methoden und Anwendungen der Mensch-Computer-Interaktion im Kontext von Sicherheit, Notfällen, Krisen, Katastrophen, Krieg und Frieden. Dies adressierend werden interaktive, mobile, ubiquitäre und kooperative Technologien sowie soziale Medien vorgestellt. Hierbei finden klassische Themen wie benutzbare (IT-)Sicherheit, Industrie 4.0, Katastrophenschutz, Medizin und Automobil, aber auch Augmented Reality, Crowdsourcing, Shitstorm Management, Social Media Analytics und Cyberwar ihren Platz. Methodisch wird das Spektrum von Usable Safety bis Usable Security Engineering von Analyse über Design bis Evaluation abgedeckt. Das Buch eignet sich ebenso als Lehrbuch für Studierende wie als Handbuch für Wissenschaftler, Designer, Entwickler und Anwender.

May 2021 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021) Conference