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  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

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.

  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

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

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 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

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

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  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

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  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

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  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

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  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

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

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

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


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  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

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

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

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

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

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

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  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

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.

  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

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.

  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

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  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

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  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

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  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

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

Introducing FreeSpeaker - A Modular Smart Home Hub Prototyping Platform

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

BibTeX DOI: 10.1145/3570361.3614080

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

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.

  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

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  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

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

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

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  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

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  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

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.

  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

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

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  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

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.

  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

BibTeX DOI: 10.1145/3544548.3580993

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

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  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

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.

  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

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.

  March 2023 Other

Lessons Learned: Koordination im Katastrophenmanagement

Michèle Knodt, Eva Platzer

PDF BibTeX DOI: 10.5281/zenodo.7756274

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


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


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.

  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

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

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

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.

  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

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  BBK Bevölkerungsschutz Article

Risikokulturen bei der Nutzung Sozialer Medien in Katastrophenlagen

Christian Reuter, Marc-André Kaufhold, Stefka Schmid

PDF BibTeX DOI: 10.26083/tuprints-00022177

Soziale Medien werden auf der ganzen Welt genutzt. Vergleicht man die allgemeine Nutzung sozialer Medien im Vereinigten Königreich (GB), Deutschland (DE), den Niederlanden (NL) und Italien (IT), zeigt sich, dass sie in Italien am wenigsten in Anspruch genommen werden. Dort sind knapp 40 % der Bevölkerung, d. h. 37 Millionen Menschen, in den sozialen Medien aktiv. Sowohl im Vereinigtem Königreich (59 %, 39 Mio.), Deutschland (55 %, 45 Mio.) als auch den Niederlanden (57 %, 9,74 Mio.) lassen sich ähnliche Tendenzen erkennen. Angesichts der Tatsache, dass mobile Endgeräte, die tendenziell immer griffbereit sind, sehr häufig zur Kommunikation über soziale Medien eingesetzt werden, ist es nicht verwunderlich, dass diese auch in Notsituationen genutzt werden [8]. Bis dato fehlt es an aussagekräftigen quantitativen und vergleichbaren Ergebnissen aus unterschiedlichen Ländern über die Wahrnehmung der Bevölkerung zur Nutzung von sozialen Medien in Notsituationen. Die im Folgenden vorgestellte Studie „The Impact of Risk Cultures: Citizens’ Perception of Social Media Use in Emergencies across Europe” [9] mit Beteiligung der TU Darmstadt, Universität Siegen und dem Tavistock Institute (London), möchte das bestehende Defizit adressieren. Anhand der repräsentativen Umfrageergebnisse werden zunächst vier europäische Länder präsentiert und dann miteinander verglichen. Ziel ist es, Ähnlichkeiten und Unterschiede in der Nutzung sozialer Medien in Notsituationen zu erfassen. Frühere Forschungsergebnisse haben im Hinblick auf Katastrophen gezeigt, dass es unterschiedliche Risikokulturen in europäischen Ländern gibt, die das Verhalten der Bevölkerung jeweils unterschiedlich beeinflussen und prägen (vgl. [4]; [5]).

  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

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  Mensch und Computer 2020 - Digitaler Wandel im Fluss der Zeit Conference

Towards Secure Urban Infrastructures: Cyber Security Challenges to Information and Communication Technology in Smart Cities

Christian Reuter, Jasmin Haunschild, Matthias Hollick, Max Mühlhäuser, Joachim Vogt, Michael Kreutzer

PDF BibTeX DOI: 10.26083/tuprints-00022179

The growth of cities continues to be a global megatrend. As more and more people live in urban areas and urban services and infrastructures are under growing strain, technologies are increasingly being researched and used to make city life more efficient and comfortable. As a result, so-called “Smart Cities” have complex IT infrastructures and cyber-physical systems such as sensor/actuator networks for the general population and are developing worldwide. Urban infrastructure must be secured against attacks, ensuring reliable and resilient services for citizens as well as privacy and data security. This paper introduces selected challenges faced by infrastructure providers, citizens and decision-makers in handling attacks aimed at information and communication technologies (ICT) of urban infrastructures and presents current research avenues for tackling cyberattacks and for developing tools for creating, portraying and disseminating actionable information as one important response to security challenges. It then presents findings from a representative survey conducted in Germany (N=1091) on the experiences and perceptions of citizens concerning the relevance of cyberattacks will be presented.

  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

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  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

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  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

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  Energy Policy Article

Power blackout: Citizens’ contribution to strengthen urban resilience

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

PDF BibTeX DOI: 10.1016/j.enpol.2023.113433

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.

  2023  DeSIRE Conference 2022 Conference

Urban Data Platforms and Urban Critical Infrastructure

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

PDF BibTeX DOI: 10.26083/tuprints-00023196

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

  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

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  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

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  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

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.

  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

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  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

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  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

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  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

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

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

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

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

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


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

Monitoring Electricity Demand Synchronization Using Copulas

Tobias Gebhard, Florian Steinke, Eva Brucherseifer

BibTeX DOI: 10.1109/ISGT-Europe54678.2022.9960369

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  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

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 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

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

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

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  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

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

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

Robust and Efficient Aggregation for Distributed Learning

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

PDF BibTeX DOI: 10.23919/EUSIPCO55093.2022.9909822

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  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

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 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

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


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

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

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

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

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  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

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

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

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  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


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

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

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

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  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

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

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

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

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

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

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

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

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  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

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  Datenschutz und Datensicherheit - DuD Article

Kollaboration im Datenschutz

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

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

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  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

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

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


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

The Water Energy Nexus: Improved Emergency Grid Restoration with DERs

Martin Pietsch, Florian Steinke


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

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.

  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

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

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

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  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

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

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

Learning Graphon Mean Field Games and Approximate Nash Equilibria

K. Cui, H. Koeppl


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


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  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

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.

  2022  The 21st Privacy Enhancing Technologies Symposium Conference

Who Can Find My Devices? Security and Privacy of Apple’s Crowd-Sourced Bluetooth Location Tracking System

Alexander Heinrich, Milan Stute, Tim Kornhuber, Matthias Hollick

PDF BibTeX DOI: 10.26083/tuprints-00020598

Overnight, Apple has turned its hundreds-of-million-device ecosystem into the world’s largest crowd-sourced location tracking network called o~ine finding (OF). OF leverages online finder devices to detect the presence of missing o~ine devices using Bluetooth and report an approximate location back to the owner via the Internet. While OF is not the first system of its kind, it is the first to commit to strong privacy goals. In particular, OF aims to ensure finder anonymity, prevent tracking of owner devices, and confidentiality of location reports. This paper presents the first comprehensive security and privacy analysis of OF. To this end, we recover the specifications of the closed-source OF protocols by means of reverse engineering. We experimentally show that unauthorized access to the location reports allows for accurate device tracking and retrieving a user’s top locations with an error in the order of 10 meters in urban areas. While we find that OF’s design achieves its privacy goals, we discover two distinct design and implementation flaws that can lead to a location correlation attack and unauthorized access to the location history of the past seven days, which could deanonymize users. Apple has partially addressed the issues following our responsible disclosure. Finally, we make our research artifacts publicly available.

  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

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  30th USENIX Security Symposium (USENIX Security 21) Conference

PrivateDrop: Practical Privacy-Preserving Authentication for Apple AirDrop

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

PDF BibTeX DOI: 10.26083/tuprints-00020599

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.

  2022  WI 2020: 15. Internationale Tagung Wirtschaftsinformatik - Zentrale Tracks Conference

Sticking with Landlines? Citizens’ Use and Perception of Social Media in Emergencies and Expectations Towards Emergency Services in Germany

Jasmin Haunschild, Marc-André Kaufhold, Christian Reuter

PDF BibTeX DOI: 10.26083/tuprints-00020743

Crisis informatics has examined the use, potentials and weaknesses of social media in emergencies across different events (e.g., man-made, natural or hybrid), countries and heterogeneous participants (e.g., citizens or emergency services) for almost two decades. While most research analyzes specific cases, few studies have focused on citizens’ perceptions of different social media platforms in emergencies using a representative sample. Basing our questionnaire on a workshop with police officers, we present the results of a representative study on citizens’ perception of social media in emergencies that we conducted in Germany. Our study suggests that when it comes to emergencies, socio-demographic differences are largely insignificant and no clear preferences for emergency services’ social media strategies exist. Due to the widespread searching behavior on some platforms, emergency services can reach a wide audience by turning to certain channels but should account for groups with distinct preferences.

  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

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  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

PDF BibTeX DOI: 10.26083/tuprints-00022163

Digitalisierung ist ein präsenter Faktor in vielen Städten. So existieren bereits viele Smart-City-Initiativen, bei denen Städte versuchen, ihre Prozesse durch Erfassung und Verknüpfung von Daten, oft unter Zuhilfenahme von Datenplattformen, zu optimieren. In Anbetracht der damit einhergehenden großen Investitionen und Veränderungen wird Bürgerbeteiligung als zentraler Faktor für den Erfolg solcher Initiativen betrachtet. Bisher ist allerdings nicht klar, was typische Beteiligungsformate von Smart-City-Initiativen sind und welche Rolle(n) BürgerInnen dabei einnehmen. Dieser Beitrag leitet mittels einer Literaturanalyse zu Smart Cities ein Kategorienschema zu typischen Bürgerbeteiligungsarten ab. Die Analyse ergab, dass sich Einbindung von BürgerInnen in politische Entscheidungen und bei der Entwicklung technischer Artefakte maßgeblich auf e‑Government oder Participatory Design bezieht. Im Hinblick auf die Beteiligungsarten zeigt sich, dass Makrofabriken, Living Labs und Open-Data-Plattformen häufige Ansätze sind, um BürgerInnen als Co-Creators einzubinden. Zudem werden BürgerInnen mit Citizen Sensing zur Erfassung von Daten oder Missständen einbezogen. Dabei zeigen sich sowohl aktivere, als auch eher passive Beteiligungsarten. Die Analyse zeigt, dass die Einbindung von BürgerInnen häufig entweder auf eine Beteiligung an politischen Entscheidungen oder an der Entwicklung technischer Artefakte abzielt. Auch wenn keine klare Abgrenzung möglich ist, sind diese Ansätze dann eher durch e‑Government oder Participatory Design inspiriert.

  2022  MuC’21: Mensch und Computer 2021 Conference

Perceptions of Police Technology Use and Attitudes Towards the Police - A Representative Survey of the German Population

Jasmin Haunschild, Christian Reuter

PDF BibTeX DOI: 10.26083/tuprints-00022173

Many Germans perceive a brutalization of society, and state officials also report feeling under attack. At the same time, policing is criticised for becoming increasingly militarised and for having extended surveillance in the course of fighting terrorism. Advancements in HCI are used in the context of many of the issues that policing is facing. In this study, we conduct a representative survey of the German population to investigate personal experiences with and attitudes towards the police and information and communication technologies (ICT) used for policing. We find an overall positive image of the police and uncritical attitudes towards ICT used for general surveillance (body-worn cameras, video surveillance, face recognition) and slightly more critical attitudes towards personal surveillance (e.g. through communication data retention). The study indicates that perceptions differ according to experience of unfair treatment by the police, while other factors such as age and education have similar effects.

  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

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  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

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.

  2022  Water Article

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

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

PDF BibTeX DOI: 10.26083/tuprints-00021223

This study presents an exploratory, historically-informed approach to assessing resilience for critical events that cause demand relocation within a water distribution system (WDS). Considering WDS as an interdependent socio-technical system, demand relocation is regarded as a critical factor that can affect resilience similarly to the more commonly analyzed component failures such as pipe leaks and pump failures. Critical events are modeled as events during which consumer nodes are evacuated within a perimeter varying in size according to a typical length scale in the studied network. The required demand drops to zero in the evacuated area, and the equivalent demand is relocated according to three sheltering schemes. Results are presented for analyzing the effect of the size of the evacuated area, the feasibility of sheltering schemes, vulnerability of particular parts of the city as well as the suitability of network nodes to accommodate relocated demand using a suitable resilience metric. The results provided by this metric are compared with those drawn from common graph-based metrics. The conclusions are critically discussed under the consideration of historical knowledge to serve as a basis for future research to refine resilience assessment of socio-technical systems.

  2022  Technische Universität Darmstadt Darmstadt Thesis

Algorithmisch gestützte Planung dezentraler Fluidsysteme

Tim Moritz Müller


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

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  30th USENIX Security Symposium (USENIX Security 21) Conference

Disrupting Continuity of Apple’s Wireless Ecosystem Security: New Tracking, DoS, and MitM Attacks on iOS and macOS Through Bluetooth Low Energy, AWDL, and Wi-Fi

Milan Stute, Alexander Heinrich, Jannik Lorenz, Matthias Hollick

PDF BibTeX DOI: 10.26083/tuprints-00020603

Apple controls one of the largest mobile ecosystems, with 1.5 billion active devices worldwide, and offers twelve proprietary wireless Continuity services. Previous works have unveiled several security and privacy issues in the involved protocols. These works extensively studied AirDrop while the coverage of the remaining vast Continuity service space is still low. To facilitate the cumbersome reverse-engineering process, we describe the first guide on how to approach a structured analysis of the involved protocols using several vantage points available on macOS. Also, we develop a toolkit to automate parts of this otherwise manual process. Based on this guide, we analyze the full protocol stacks involved in three Continuity services, in particular, Handoff (HO), Universal Clipboard (UC), and Wi-Fi Password Sharing (PWS). We discover several vulnerabilities spanning from Bluetooth Low Energy (BLE) advertisements to Apple’s proprietary authentication protocols. These flaws allow for device tracking via HO’s mDNS responses, a denial-of-service (DoS) attack on HO and UC, a DoS attack on PWS that prevents Wi-Fi password entry, and a machine-in-the-middle (MitM) attack on PWS that connects a target to an attacker-controlled Wi-Fi network. Our PoC implementations demonstrate that the attacks can be mounted using affordable off-the-shelf hardware ($20 micro:bit and a Wi-Fi card). Finally, we suggest practical mitigations and share our findings with Apple, who have started to release fixes through iOS and macOS updates.

  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

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  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

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  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

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  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

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  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

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

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 user�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

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.

  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

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  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


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

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  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

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

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

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

  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

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

  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

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

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

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.

  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

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  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

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.

  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


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 Other

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

Alice Knauf, Michèle Knodt

PDF BibTeX DOI: 10.5281/zenodo.5082350

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

  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

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  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

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.

  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


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

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.

  April 2021  IEEE Transactions on Mobile Computing Article

Performance and Pitfalls of 60 GHz WLANs Based on Consumer-Grade Hardware

Swetank Kumar Saha, Hany Assasa, Adrian Loch, Naveen Muralidhar Prakash, Roshan Shyamsunder, Shivang Aggarwal, Daniel Steinmetzer, Dimitrios Koutsonikolas, Joerg Widmer, Matthias Hollick

PDF BibTeX DOI: 10.1109/TMC.2020.2967386

Wireless networks operating in the 60 GHz band have the potential to provide very high throughput but face a number of challenges (e.g., high attenuation, beam training, and coping with mobility) which are widely accepted but often not well understood in practice. Understanding these challenges, and especially their actual impact on consumer-grade hardware is fundamental to fully exploit the high physical layer rates in the 60 GHz band. To this end, we perform an extensive measurement campaign using two commercial off-the-shelf 60 GHz routers in real-world environments. Our results allow us to revisit a range of issues and provide much deeper insights into the reasons for specific performance compared to prior work on performance characterization. Further, our study goes beyond basic link characterization and explores for the first time practical considerations such as coverage and access point deployment. While some of our observations are expected, we also obtain highly surprising insights that challenge the prevailing wisdom in the community. We derive the shortcomings of current commercial 60 GHz devices, and the fundamental problems that remain open on the way to fast and efficient 60 GHz networking.

  March 2021   VDI/VDE Mechatronik‐Tagung 2021 Conference

Entwicklung eines autonomiefokussierten hochmobilen Bodenrobotersystems für den Katastrophenschutz

Marius Schnaubelt, Tobias Ullrich, Moritz Torchalla, Jonas Diegelmann, Matthias Hoffmann, Oskar von Stryk


Mobile Rettungsroboter ermöglichen den menschlichen Bedienern die Bearbeitung von Aufgaben aus sicherer Entfernung in risikoreichen Umgebungen. Durch die unstrukturierte Umgebung der komplexen und vorab unbekannten Einsatzszenarien, verursacht die aktuell übliche Teleoperation der Robotersysteme eine hohe kognitive Belastung für den Roboteroperator, was schnell zur Ermüdung führt. Durch intelligente autonome Assistenzfunktionen können die Operatoren entlastet werden, wodurch die Wahrscheinlichkeit von Bedienfehlern reduziert und die Effizienz des Robotereinsatzes erhöht werden kann. Diese innovativen Assistenzfunktionen benötigen jedoch ein mechatronisches Design, dessen Anforderungen an Hard- und Software für ein effektives Gesamtsystem eng aufeinander abgestimmt und umgesetzt werden müssen. Die Entwicklung eines hochmobilen autonomiefokussierten Bodenroboters mit modularen Sensornutzlasten ermöglicht dem Operator ein umfassendes Situationsbewusstsein sowie Unterstützung bei Navigation und Manipulation. Die Evaluation des Gesamtsystems und von Einzelkomponenten analysiert die Erfüllung des Anforderungskatalogs und demonstriert so die Eignung für (semi-)autonome Rettungsrobotikeinsätze.

  2021 Other

Transformation, Zirkulation, System of Systems : Für ein dynamisches Verständnis netzgebundener Infrastrukturen

Jens Ivo Engels, Sybille Frank, Iryna Gurevych, Martina Heßler, Michèle Knodt, Jochen Monstadt, Alfred Nordmann, Andreas Oetting, Annette Rudolph-Cleff, Uwe Rüppel, Gerrit Jasper Schenk, Florian Steinke

PDF BibTeX DOI: 10.26083/tuprints-00017923

Der Aufsatz plädiert dafür, die Dynamik technischer netzgebundener Infrastrukturen mit einem dreifachen Ansatz zu untersuchen: Transformation, Zirkulation und System of Systems. Transformation repräsentiert dabei die Veränderung von Infrastrukturen als Gesamtsysteme. Zirkulation repräsentiert die in jeder Infrastrukturfunktion eingeschriebene Dynamik. Gemeint ist der Austausch von Gütern, Menschen, Informationen oder Energie in Netzen. System of Systems ist ein Konzept zur Beschreibung der Interdependenzen verschiedener verbundener Sektoren und Systeme. Die drei Merkmale der Transformation sind aufeinander bezogen: Das Konzept des System of System liefert einen Erklärungsansatz, auf welchen Wegen Zirkulation stattfindet (in und zwischen Netzwerken) und welche Faktoren die Zirkulation beeinflussen. Diese Phänomene können wiederum als Ursachen oder Anreize für Transformation auf der Systemebene begriffen werden. Umgekehrt ist zu fragen, inwieweit Transformationsprozesse der Infrastrukturnetze die Konfiguration des System of Systems verändern.

  2021  24th International Conference on Artificial Intelligence and Statistics Conference

Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning

Kai Cui, Heinz Koeppl


The recent mean field game (MFG) formalism facilitates otherwise intractable computation of approximate Nash equilibria in many-agent settings. In this paper, we consider discrete-time finite MFGs subject to finite-horizon objectives. We show that all discrete-time finite MFGs with non-constant fixed point operators fail to be contractive as typically assumed in existing MFG literature, barring convergence via fixed point iteration. Instead, we incorporate entropy-regularization and Boltzmann policies into the fixed point iteration. As a result, we obtain provable convergence to approximate fixed points where existing methods fail, and reach the original goal of approximate Nash equilibria. All proposed methods are evaluated with respect to their exploitability, on both instructive examples with tractable exact solutions and high-dimensional problems where exact methods become intractable. In high-dimensional scenarios, we apply established deep reinforcement learning methods and empirically combine fictitious play with our approximations.

  2021  60th Conference on Decision and Control (CDC2021) Conference

Discrete-Time Mean Field Control with Environment States

K. Cui, A. Tahir, M. Sinzger, H. Koeppl


Multi-agent reinforcement learning methods have shown remarkable potential in solving complex multi-agent problems but mostly lack theoretical guarantees. Recently, mean field control and mean field games have been established as a tractable solution for large-scale multi-agent problems with many agents. In this work, driven by a motivating scheduling problem, we consider a discrete-time mean field control model with common environment states. We rigorously establish approximate optimality as the number of agents grows in the finite agent case and find that a dynamic programming principle holds, resulting in the existence of an optimal stationary policy. As exact solutions are difficult in general due to the resulting continuous action space of the limiting mean field Markov decision process, we apply established deep reinforcement learning methods to solve the associated mean field control problem. The performance of the learned mean field control policy is compared to typical multi-agent reinforcement learning approaches and is found to converge to the mean field performance for sufficiently many agents, verifying the obtained theoretical results and reaching competitive solutions

  2021  2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) Conference

HectorGrapher: Continuous-time Lidar SLAM with Multi-resolution Signed Distance Function Registration for Challenging Terrain

Kevin Daun, Marius Schnaubelt, Stefan Kohlbrecher, Oskar von Stryk


For deployment in previously unknown, unstructured, and GPS-denied environments, autonomous mobile rescue robots need to localize themselves in such environments and create a map of it using a simultaneous localization and mapping (SLAM) approach. Continuous-time SLAM approaches represent the pose as a time-continuous estimate that provides high accuracy and allows correcting for distortions induced by motion during the scan capture. To enable robust and accurate real-time SLAM in challenging terrain, we propose HectorGrapher which enables accurate localization by continuous-time pose estimation and robust scan registration based on multi-resolution signed distance functions. We evaluate the method in multiple publicly available real-world datasets, as well as a data set from the RoboCup 2021 Rescue League, where we applied the proposed method to win the Best-in-Class “Exploration and Mapping” Award.

  2021  Water Article

Optimal Resilience Enhancement of Water Distribution Systems

Imke-Sophie Lorenz, Peter F. Pelz

PDF BibTeX DOI: 10.26083/tuprints-00019245

Water distribution systems (WDSs) as critical infrastructures are subject to demand peaks due to daily consumption fluctuations, as well as long term changes in the demand pattern due to increased urbanization. Resilient design of water distribution systems is of high relevance to water suppliers. The challenging combinatorial problem of high-quality and, at the same time, low-cost water supply can be assisted by cost-benefit optimization to enhance the resilience of existing main line WDSs, as shown in this paper. A Mixed Integer Linear Problem, based on a graph-theoretical resilience index, is implemented considering WDS topology. Utilizing parallel infrastructures, specifically those of the urban transport network and the water distribution network, makes allowances for physical constraints, in order to adjust the existing WDS and to enhance resilience. Therefore, decision-makers can be assisted in choosing the optimal adjustment of WDS depending on their investment budget. Furthermore, it can be observed that, for a specific urban structure, there is a convergence of resilience enhancement with higher costs. This cost-benefit optimization is conducted for a real-world main line WDS, considering also the limitations of computational expenses.

  2021  2021 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe) Conference

Evaluation of Multiparametric Linear Programming for Economic Dispatch under Uncertainty

Johannes Sindt, Allan Santos, Marc E. Pfetsch, Florian Steinke


For risk assessment purposes, we study how economic dispatch decisions vary with the uncertain input factors that may arise, e.g., from the use of variable renewable energies. Given a known random input distribution and linear programming (LP)-based dispatch, we aim to describe the distribution of the resulting variables and objective values. Relying on Monte Carlo simulation (MCS) is computationally expensive, especially if the uncertain factors are high dimensional. In this paper we evaluate an algorithm using multiparametric linear programming (MPLP) for this purpose. It avoids solving an LP for every sample of the random vector by characterizing the parametric LP solution as a piece-wise linear function whose pieces can be stored for repeated use. We compare the algorithm with MCS and other quasi-Monte Carlo sampling approaches for three economic dispatch use cases with varying complexity. The MPLP approach is as accurate as MCS, but up to 300 times faster for the merit order use case.

  2021  WiSec ‘20: 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks Conference

DEMO: BTLEmap: Nmap for Bluetooth Low Energy

Alexander Heinrich, Milan Stute, Matthias Hollick

PDF BibTeX DOI: 10.26083/tuprints-00017839

The market for Bluetooth Low Energy (BLE) devices is booming and, at the same time, has become an attractive target for adversaries. To improve BLE security at large, we present BTLEmap, an auditing application for BLE environments. BTLEmap is inspired by network discovery and security auditing tools such as Nmap for IP-based networks. It allows for device enumeration, Generic Attribute Profile (GATT) service discovery, and device fingerprinting. It also features a BLE advertisement dissector, data exporter, and a user-friendly UI including a proximity view. BTLEmap currently runs on iOS and macOS using Apple’s CoreBluetooth API but also accepts alternative data inputs such as a Raspberry Pi to overcome the restricted vendor API. The open-source project is under active development and will provide more advanced capabilities such as long-term device tracking (in spite of MAC address randomization) in the future.

  2021  IEEE Transactions on Dependable and Secure Computing Article

RESCUE: A Resilient and Secure Device-to-Device Communication Framework for Emergencies

Milan Stute, Florian Kohnhauser, Lars Baumgärtner, Lars Almon, Matthias Hollick, Stefan Katzenbeisser, Bernd Freisleben

PDF BibTeX DOI: 10.26083/tuprints-00017838

During disasters, existing telecommunication infrastructures are often congested or even destroyed. In these situations, mobile devices can form a backup communication network for civilians and emergency services using disruption-tolerant networking (DTN) principles. Unfortunately, such distributed and resource-constrained networks are particularly susceptible to a wide range of attacks such as terrorists trying to cause more harm. In this paper, we present RESCUE, a resilient and secure device-to-device communication framework for emergency scenarios that provides comprehensive protection against common attacks. RESCUE features a minimalistic DTN protocol that, by design, is secure against notable attacks such as routing manipulations, dropping, message manipulations, blackholing, or impersonation. To further protect against message flooding and Sybil attacks, we present a twofold mitigation technique. First, a mobile and distributed certificate infrastructure particularly tailored to the emergency use case hinders the adversarial use of multiple identities. Second, a message buffer management scheme significantly increases resilience against flooding attacks, even if they originate from multiple identities, without introducing additional overhead. Finally, we demonstrate the effectiveness of RESCUE via large-scale simulations in a synthetic as well as a realistic natural disaster scenario. Our simulation results show that RESCUE achieves very good message delivery rates, even under flooding and Sybil attacks.

  2021  2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) Conference

Robust Multisensor Fusion for Reliable Mapping and Navigation in Degraded Visual Conditions

Moritz Torchalla, Marius Schnaubelt, Kevin Daun, Oskar von Stryk


We address the problem of robust simultaneous mapping and localization in degraded visual conditions using low-cost off-the-shelf radars. Current methods often use high- end radar sensors or are tightly coupled to specific sensors, limiting the applicability to new robots. In contrast, we present a sensor-agnostic processing pipeline based on a novel forward sensor model to achieve accurate updates of signed distance function-based maps and robust optimization techniques to reach robust and accurate pose estimates. Our evaluation demonstrates accurate mapping and pose estimation in indoor environments under poor visual conditions and higher accuracy compared to existing methods on publicly available benchmark data.

  2021  30th USENIX Security Symposium Conference

Disrupting Continuity of Apple’s Wireless Ecosystem Security: New Tracking, DoS, and MitM Attacks on iOS and macOS Through Bluetooth Low Energy, AWDL, and Wi-Fi

Milan Stute, Alexander Heinrich, Jannik Lorenz, Matthias Hollick


Apple controls one of the largest mobile ecosystems, with 1.5 billion active devices worldwide, and offers twelve proprietary wireless Continuity services. Previous works have unveiled several security and privacy issues in the involved protocols. These works extensively studied AirDrop while the coverage of the remaining vast Continuity service space is still low. To facilitate the cumbersome reverse-engineering process, we describe the first guide on how to approach a structured analysis of the involved protocols using several vantage points available on macOS. Also, we develop a toolkit to automate parts of this otherwise manual process. Based on this guide, we analyze the full protocol stacks involved in three Continuity services, in particular, Handoff (HO), Universal Clipboard (UC), and Wi-Fi Password Sharing (PWS). We discover several vulnerabilities spanning from Bluetooth Low Energy (BLE) advertisements to Apple’s proprietary authentication protocols. These flaws allow for device tracking via HO’s mDNS responses, a denial-of-service (DoS) attack on HO and UC, a DoS attack on PWS that prevents Wi-Fi password entry, and a machine-in-the-middle (MitM) attack on PWS that connects a target to an attacker-controlled Wi-Fi network. Our PoC implementations demonstrate that the attacks can be mounted using affordable off-the-shelf hardware ($20 micro:bit and a Wi-Fi card). Finally, we suggest practical mitigations and share our findings with Apple, who have started to release fixes through iOS and macOS updates.

  December 2020  IEEE Radar Conference Conference

An Unsupervised Approach for Graph-based Robust Clustering of Human Gait Signatures

A. Taştan, M. Muma, A. M. Zoubir

BibTeX DOI: 10.1109/RadarConf2043947.2020.9266313

Classification of gait abnormalities plays a key role in medical diagnosis, sports, physiotherapy and rehabilitation. We demonstrate the effectiveness of a new graph construction-based outlier detection method and and the applicability of a new parameter-free clustering approach on radar-based human gait signatures. Micro-Doppler radar-based human gait signatures of ten test subjects for five different gait types consisting of normal, simulated abnormal and assisted walks are clustered using five different clustering algorithms. The proposed algorithm outperforms existing methods both in cluster enumeration and partition and achieves an overall correct clustering rate of 92.8%. The developed method is promising for performing medical diagnosis in a robust unsupervised fashion.

  November 2020  45th Local Computer Networks Symposium on Emerging Topics in Networking Conference

Topology-aware Path Planning for In-Transit Coverage of Aerial Post-Disaster Communication Assistance Systems

Julian Zobel, Benjamin Becker, Ralf Kundel, Patrick Lieser, Ralf Steinmetz

BibTeX DOI: 10.1109/LCNSymposium50271.2020.9363268

The increase in natural disasters that impair and destroy communication infrastructure over the last years simultaneously increased the importance of infrastructure-independent ad hoc communication. Especially delay-tolerant networks (DTNs) are able to provide basic communication functionality for civilians, but performance suffers from a typically highly intermittent network with clusters around important locations like shelters. Small Unmanned Aerial Vehicles (UAVs) have proven to be efficient data ferries between clusters, but they require knowledge of cluster locations and also do not cover network nodes in transit between clusters. These in-transit nodes are therefore disconnected from the network for a long time and might miss critical messages like evacuation notices or hazard warnings. This paper provides two contributions for UAV-assisted post-disaster DTN communication. First, we present a novel approach to estimate the location of dynamically changing clusters in a post-disaster scenario. Second, we introduced a topology-aware path planning approach for UAV data ferry flights, covering in-transit node on their way between clusters. Our evaluation results highlight the quality requirements on topology information for an efficient application of Aerial Post-Disaster Communication Assistance Systems and demonstrate the positive impact of in-transit node coverage on the DTN’s communication performance.

  November 2020  SPLASH ‘20: Conference on Systems, Programming, Languages, and Applications, Software for Humanity Conference

ConSysT: Tunable, Safe Consistency Meets Object-Oriented Programming

Mirko Köhler, Nafise Eskandani, Alessandro Margara, Guido Salvaneschi

BibTeX DOI: 10.1145/3427761.3428346

Data replication is essential in scenarios like geo-distributed datacenters, but poses challenges for data consistency. Developers adopt Strong consistency at the cost of performance or embrace Weak consistency and face a higher programming complexity. We argue that languages should associate consistency to data types. We present , a programming language and middleware that provides abstractions to specify consistency types, enabling mixing different consistency levels in the same application. Such mechanism is fully integrated with object-oriented programming and type system guarantees that different levels can only be mixed correctly.

  November 2020  Proceedings of the ACM on Programming Languages Article

Rethinking Safe Consistency in Distributed Object-Oriented Programming

Mirko Köhler, Nafise Eskandani, Pascal Weisenburger, Alessandro Margara, Guido Salvaneschi

BibTeX DOI: 10.1145/3428256

Large scale distributed systems require to embrace the trade off between consistency and availability, accepting lower levels of consistency to guarantee higher availability. Existing programming languages are, however, agnostic to this compromise, resulting in consistency guarantees that are the same for the whole application and are implicitly adopted from the middleware or hardcoded in configuration files. In this paper, we propose to integrate availability in the design of an object-oriented language, allowing developers to specify different consistency and isolation constraints in the same application at the granularity of single objects. We investigate how availability levels interact with object structure and define a type system that preserves correct program behavior. Our evaluation shows that our solution performs efficiently and improves the design of distributed applications.

  September 2020  Proceedings of the 14th International Workshop on Wireless Network Testbeds, Experimental evaluation & Characterization Conference

Hardware-Accelerated Real-Time Stream Data Processing on Android with GNU Radio

Bastian Bloessl, Lars Baumgärtner, Matthias Hollick

PDF BibTeX DOI: 10.1145/3411276.3412184

With the ever-increasing performance of smartphones and tablets, they become viable platforms for applications that were, in the past, only possible on desktops or laptops. In this paper, we study their applicability for real-time stream-data processing, which is particularly interesting for Software Defined Radio (SDR) applications, enabling wireless measurement and experimentation campaigns on mobile platforms. To this end, we port GNU Radio, a state-of-theart, open source, real-time stream-data processing framework, to Android and evaluate its performance. We show that it is possible to fully benefit from available accelerators, i.e., Single Instruction Multiple Data (SIMD) and the Graphics Processing Unit (GPU), which provide considerable speedups and allow for efficient implementations. As a general-purpose real-time data processing framework, GNU Radio can provide the base for a wide range of applications. To demonstrate its flexibility, we provide example applications that implement FM and Wireless LAN (WLAN). Our toolchain is published as open source software, thus serving as an enabler for highly mobile SDR applications.

  August 2020  Datenschutz und Datensicherheit (DuD) Article

Datensicherheit von Corona-Apps nach der DSGVO

Tim Grube, Alexander Heinrich, Jan-Philipp Stroscher, Sabrina Schomberg

BibTeX DOI: 10.1007/s11623-020-1314-0

Der Beitrag analysiert die Protokolle der Konsortien DP-3T und PEPP-PT aus technischer Perspektive und grenzt diese voneinander ab. Zudem wird die technische Ausgestaltung der Entwicklerschnittstelle (API) von Google und Apple dargestellt. Aufbauend darauf erfolgt eine rechtliche Beurteilung der sich aus Art. 5 Abs. 1 lit. f, 25, 32 DSGVO ergebenden und die Datensicherheit betreffenden Kriterien und deren konkrete Umsetzung in den Protokollen.

  July 2020  13th ACM Conference on Security and Privacy in Wireless and Mobile Networks Conference

Acoustic Integrity Codes: Secure Device Pairing Using Short-Range Acoustic Communication

Florentin Putz, Flor Álvarez, Jiska Classen

PDF BibTeX DOI: 10.1145/3395351.3399420

Secure Device Pairing (SDP) relies on an out-of-band channel to authenticate devices. This requires a common hardware interface, which limits the use of existing SDP systems. We propose to use short-range acoustic communication for the initial pairing. Audio hardware is commonly available on existing off-the-shelf devices and can be accessed from user space without requiring firmware or hardware modifications. We improve upon previous approaches by designing Acoustic Integrity Codes (AICs): a modulation scheme that provides message authentication on the acoustic physical layer. We analyze their security and demonstrate that we can defend against signal cancellation attacks by designing signals with low autocorrelation. Our system can detect overshadowing attacks using a ternary decision function with a threshold. In our evaluation of this SDP scheme’s security and robustness, we achieve a bit error ratio below 0.1% for a net bit rate of 100 bps with a signal-to-noise ratio (SNR) of 14 dB. Using our open-source proof-of-concept implementation on Android smartphones, we demonstrate pairing between different smartphone models.

  June 2020  International Journal of Mechanics and Control Article

Optimization-Based Planning for Autonomous Traversal of Obstacles with Mobile Ground Robots

Martin Oehler, Stefan Kohlbrecher, Oskar von Stryk


Mobile robotic platforms which are traversing unstructured environments with challenging uneven terrain are permanently endangered of falling over. Previous research on trajectory planning methods for the prevention of vehicle tip-over is mostly limited to basic mobility systems with only few degrees of freedom (DOF). This paper proposes a novel optimization-based planning approach that enables mobile robots to autonomously traverse obstacles and rough terrain more safely. A 3D world model as provided from external sensors like Lidar is used to compute a whole-body motion plan in advance by optimizing the trajectories of each joint. Active flipper tracks maximize ground contact for improved traction and, if available, manipulator arm joints are used to further improve stability metrics. Additional constraints prevent collisions with the environment and the robot itself. The presented approach makes only few assumptions about the robot’s configuration and is applicable to a wide range of wheeled or tracked platforms. This is demonstrated by experimental evaluation for two different robots in simulation and for one physical robot. In four different test scenarios it is shown, that the proposed approach effectively prevents vehicle tip-over during traversal of uneven ground.

  June 2020  International Journal of Disaster Risk Reduction (IJDRR) Article

Emergency service staff and social media – A comparative empirical study of the attitude by Emergency Services staff in Europe in 2014 and 2017

Christian Reuter, Marc-André Kaufhold, Fabian Spahr, Thomas Spielhofer, Anna Sophie Hahne

PDF BibTeX DOI: 10.1016/j.ijdrr.2020.101516

Finding a way to ensure an effective use of social media has become increasingly important to emergency services over the past decade. Despite all efforts to determine the utility of social media for emergency organisations, it is necessary to benefit from such institutions’ staffs’ opinions to establish effective use. To provide empirical evidence we present a comparison of two surveys, conducted across Europe with emergency services in 2014 and 2017 respectively, with a total of 1169 answers. The analysis shows that personal experience has an effect on how organisational usage of social media is perceived and how emergency service staff view the future use of social media. Furthermore, the use has increased. This article not only shows emergency services what their staff think about their social media usage but also discusses challenges and future directions for the design of systems that can be useful for further development of optimized organisational social media usage.

  May 2020  28th European Conference on Information Systems Conference

Warning the Public: A Survey on Attitudes, Expectations and Use of Mobile Crisis Apps in Germany

Marc-André Kaufhold, Jasmin Haunschild, Christian Reuter


As part of information systems, the research field of crisis informatics increasingly investigates the potentials and limitations of mobile crisis apps, which constitute a relatively new public service for citizens and are specifically designed for the dissemination of disaster‐related information and communication between authorities, organizations and citizens. While existing crisis apps, such as KATWARN or NINA in Germany, focus on preparatory information and warning functionality, there is a need for apps and research on police-related functionality, such as information on cybercrime, fraud offences, or search for missing persons. Based on a workshop with civil protection (N=12) and police officers (N=15), we designed a questionnaire and conducted a representative survey of German citizens (N=1.219) on the past, current and future use, perceived helpfulness, deployment and behavioural preferences, configurability and most important functionality of mobile crisis apps. Our results indicate that in addition to emergency and weather warnings, crime- and health-related warnings are also desired by many, as is the possibility for bidirectional communication. People also want one central app and are resistant to installing more than one crisis app. Furthermore, there are few significant differences between socioeconomic groups.

  May 2020  17th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2020) Conference

LoRa-based Device-to-Device Smartphone Communication for Crisis Scenarios

Jonas Höchst, Lars Baumgärtner, Franz Kuntke, Alvar Penning, Artur Sterz, Bernd Freisleben


In this paper, we present an approach to facilitate long-range device-to-device communication via smartphones in crisis scenarios. Through a custom firmware for low-cost LoRa capable micro-controller boards, called rf95modem, common devices for end users can be enabled to use LoRa through a Bluetooth, Wi-Fi, or serial connection. We present two applications utilizing the flexibility provided by the proposed firmware. First, we introduce a novel device-to-device LoRa chat application that works a) on the two major mobile platforms Android and iOS and b) on traditional computers like notebooks using a console-based interface. Second, we demonstrate how other infrastructure-less technology can benefit from our approach by integrating it into the DTN7 delay-tolerant networking software. The firmware, the device-to-device chat application, the integration into DTN7, as well as the experimental evaluation code fragments are available under permissive open-source licenses.

  April 2020  Proceedings of the International Conference on Wirtschaftsinformatik (WI) Conference

Sticking with Landlines? Citizens’ Use and Perception of Social Media in Emergencies and Expectations Towards Emergency Services in Germany

Jasmin Haunschild, Marc-André Kaufhold, Christian Reuter

BibTeX DOI: 10.30844/wi_2020_o2-haunschild

Crisis informatics has examined the use, potentials and weaknesses of social media in emergencies across different events (e.g., man-made, natural or hybrid), countries and heterogeneous participants (e.g., citizens or emergency services) for almost two decades. While most research analyzes specific cases, few studies have focused on citizens’ perceptions of different social media platforms in emergencies using a representative sample. Basing our questionnaire on a workshop with police officers, we present the results of a representative study on citizens’ perception of social media in emergencies that we conducted in Germany. Our study suggests that when it comes to emergencies, socio-demographic differences are largely insignificant and no clear preferences for emergency services’ social media strategies exist. Due to the widespread searching behavior on some platforms, emergency services can reach a wide audience by turning to certain channels but should account for groups with distinct preferences.

  April 2020  2020 CHI Conference on Human Factors in Computing Systems Conference

Walk The Line: Leveraging Lateral Shifts of the Walking Path as an Input Modality for Head-Mounted Displays

Florian Müller, Martin Schmitz, Daniel Schmitt, Sebastian Günther, Markus Funk, Max Mühlhäuser

BibTeX DOI: 10.1145/3313831.3376852

Recent technological advances have made head-mounted displays (HMDs) smaller and untethered, fostering the vision of ubiquitous interaction in a digitally augmented physical world. Consequently, a major part of the interaction with such devices will happen on the go, calling for interaction techniques that allow users to interact while walking. In this paper, we explore lateral shifts of the walking path as a hands-free input modality. The available input options are visualized as lanes on the ground parallel to the user’s walking path. Users can select options by shifting the walking path sideways to the respective lane. We contribute the results of a controlled experiment with 18 participants, confirming the viability of our approach for fast, accurate, and joyful interactions. Further, based on the findings of the controlled experiment, we present three example applications.

  March 2020  Technische Universität Darmstadt Darmstadt Thesis

Secure device-to-device communication for emergency response

Flor Álvarez

PDF BibTeX DOI: 10.25534/tuprints-00011486

Mobile devices have the potential to make a significant impact during disasters. However, their practical impact is severely limited by the loss of access to mobile communication infrastructure: Precisely, when there is a surge in demand for communications from people in a disaster zone, this capacity for communications is severely curtailed. This loss of communications undermines the effectiveness of the many recent innovations in the use of smartphones and similar devices to mitigate the effects of disasters. While various solutions have been proposed, e. g., by having handsets form wireless ad hoc networks, none are complete: Some are specific to certain mobile operating systems or operating system versions. Others result in unacceptably increased energy consumption, flattening the batteries of phones at a time when users need to conserve energy due to the loss of access to opportunities to recharge their mobile devices. Realistic user behaviour, including patterns of movement and communications, are also rarely addressed. Further, security is rarely considered in a comprehensive and satisfying manner, leaving users exposed to a variety of potential attacks. Thus there is a compelling need to find more effective solutions for communications, energy management, and security of mobile devices operating in disaster conditions. To address these shortcomings, this thesis provides a suite of comprehensive solutions that contribute to facilitate secure device-to-device communication for emergency response. This thesis works to solve these problems by: (i) Conducting a large-scale field-trial to understand and analyze civilians’ behaviour during disaster scenarios; (ii) Proposing a practical, lightweight scheme for bootstrapping device-to-device security, that is tailored for local urban operations representative of disaster scenarios; (iii) Realizing novel energy management strategies for the neighbour discovery problem, which deliver significant energy savings in return for only a minimal reduction in neighbour discovery efficiency; (iv) The description of novel concepts for using devices in a smart city environment that remain functional following a disaster to support communications among mobile devices. In short, this thesis adds considerably to the understanding of the difficulties in the formation of direct device-to-device communications networks composed primarily of civilians’ mobile devices, and how several facets of this problem can be mitigated. Several of the proposed enhancements are also implemented. Thus, this thesis also takes essential steps in the direction of realizing such solutions to demonstrate their feasibility on real devices, intending to improve the tools available to civilians post-disaster.

  February 2020  The Art, Science, and Engineering of Programming Article

Implementing a Language for Distributed Systems: Choices and Experiences with Type Level and Macro Programming in Scala

Pascal Weisenburger, Guido Salvaneschi

PDF BibTeX DOI: 10.22152/

Multitier programming languages reduce the complexity of developing distributed systems by developing the distributed system in a single coherent code base. The compiler or the runtime separate the code for the components of the distributed system, enabling abstraction over low level implementation details such as data representation, serialization and network protocols. Our ScalaLoci language allows developers to declare the different components and their architectural relation at the type level, allowing static reasoning about about distribution and remote communication and guaranteeing static type safety across components. The compiler splits the multitier program into the component-specific code and automatically generates the communication boilerplate. Communication between components can be modeled by declaratively specifying data flows between components using reactive programming. In this paper, we report on the implementation of our design and our experience with embedding our language features into Scala as a host language. We show how a combination of Scala’s advanced type level programming and its macro system can be used to enrich the language with new abstractions. We comment on the challenges we encountered and the solutions we developed for our current implementation and outline suggestions for an improved macro system to support the such use cases of embedding of domain-specific abstractions.

  February 2020  Embedded Wireless Systems and Networks (EWSN) Conference

Improving the Reliability of Bluetooth Low Energy Connections

Michael Spörk, Jiska Classen, Carlo Alberto Boano, Matthias Hollick, Kay Römer


o sustain a reliable data exchange, applications based on Bluetooth Low Energy (BLE) need to effectively blacklist channels and adapt the physical mode of an active connection at runtime. Although the BLE specification foresees the use of these two mechanisms, their implementation is left up to the radio vendors and has not been studied in detail yet. This paper fills this gap: we first investigate experimentally how to assess the quality of a BLE connection at runtime using information gathered from the radio. We then show how this information can be used to promptly blacklist poor channels and select a physical mode that sustains a high link-layer reliability while minimizing power consumption. We implement both mechanisms on two popular platforms and show experimentally that they allow to significantly improve the reliability of BLE connections, with a reduction in packet loss by up to 22 % compared to existing solutions.

  February 2020  Technische Universität Darmstadt Darmstadt Thesis

Availability by Design: Practical Denial-of-Service-Resilient Distributed Wireless Networks

Milan Stute

PDF BibTeX DOI: 10.25534/tuprints-00011457

Distributed wireless networks (DWNs) where devices communicate directly without relying on Internet infrastructure are on the rise, driving new applications and paradigms such as multimedia, authentication, payment, Internet of things (IoT), vehicular communication, and emergency response. However, the increased societal reliance on technology and the resulting “always-on” expectations of the users increase the risk of denial-of-service (DoS) attacks as they can leverage disruption in new ways beyond extortions (ransomware) that are common in today’s Internet ecosystem. These new risks extend to our physical world, directly impacting our daily lives, e.g., by being locked out of a smart home or by disrupting vehicular collision avoidance systems. As a research community, we need to protect those new applications that—as we find—can be mapped to a total of three distinct networking scopes: neighbor, island, and archipelago. In this thesis, we advance the field in each of these scopes. First, we analyze two proprietary neighbor communication protocols, Apple Wireless Direct Link (AWDL) and Apple AirDrop, that are deployed on more than 1.4 billion devices worldwide. During the process, we uncover several security and privacy vulnerabilities ranging from design flaws to implementation bugs leading to a machine-in-the-middle (MitM) attack on AirDrop, a DoS attack on AWDL preventing communication, and DoS attacks enabling crashing of neighboring devices. In addition, we found privacy leaks that enable user identification and long-term tracking. All attacks can be mounted using low-cost off-the-shelf hardware. In total, we disclose eight distinct vulnerabilities that we mitigate in collaboration with Apple. Second, we design and implement a new island communication protocol tailored to IoT scenarios, which provides provable protections against previously neglected risks such as wormhole- and replay-supported greyhole attacks. We support our analytical findings with testbed experiments. Third, we propose an archipelago-scope communication framework for emergencies that achieves resiliency against flooding and Sybil attacks. We evaluate our design using an original expert knowledge-based simulation that models human mobility during the aftermath of the 2013 Typhoon Haiyan in the Philippines. Finally, and to nourish future research, we provide a guide for analyzing Apple’s wireless ecosystem and publish several software artifacts, including an AWDL Wireshark dissector, open AWDL and AirDrop implementations, a prototype of our IoT communication protocol, and our natural disaster mobility model.

  2020  IEEE Internet of Things Journal Article

LIDOR: A Lightweight DoS-Resilient Communication Protocol for Safety-Critical IoT Systems

Milan Stute, Pranay Agarwal, Abhinav Kumar, Arash Asadi, Matthias Hollick

PDF BibTeX DOI: 10.25534/tuprints-00013320

IoT devices penetrate different aspects of our life including critical services, such as health monitoring, public safety, and autonomous driving. Such safety-critical IoT systems often consist of a large number of devices and need to withstand a vast range of known Denial-of-Service (DoS) network attacks to ensure a reliable operation while offering low-latency information dissemination. As the first solution to jointly achieve these goals, we propose LIDOR, a secure and lightweight multihop communication protocol designed to withstand all known variants of packet dropping attacks. Specifically, LIDOR relies on an end-to-end feedback mechanism to detect and react on unreliable links and draws solely on efficient symmetric-key cryptographic mechanisms to protect packets in transit. We show the overhead of LIDOR analytically and provide the proof of convergence for LIDOR which makes LIDOR resilient even to strong and hard-to-detect wormhole-supported grayhole attacks. In addition, we evaluate the performance via testbed experiments. The results indicate that LIDOR improves the reliability under DoS attacks by up to 91% and reduces network overhead by 32% compared to a state-of-the-art benchmark scheme.

  2020  Disaster Research Days 2020 Conference