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Conference on Local computer networks (LCN)

作者： Jonas Schulz Hristina Radak Phuong T. Nguyen Giang T. Nguyen Frank H. P. Fitzek Deutsche Telekom Chair of Communication Networks TU Dresden Germany Department of Information Engineering Computer Science and Mathematics University of L’Aquila Italy Haptic Communication Systems TU Dresden Germany Centre for Tactile Internet with Human-in-the-Loop (CeTI)

Human activity recognition is crucial for tactile internet, virtual reality, and digital-twin applications. Previous works have analyzed machine learning for this purpose but often need to pay more attention to typical challenges when deploying these machine learning models in production. First, data must be transmitted to the network node on which the machine-learning model is running. However, scaling human activity recognition by the number of devices and users puts additional constraints on the available transmission channel. While transmitting less data saves bandwidth, removing redundant information from the data can also benefit machine learning. This paper addresses the problem of transmitting wearable sensor data for human activity recognition. To this end, we analyze the extent to which wearable sensor data can be compressed via sparse coding without sacrificing loss in recognition performance. We empirically illustrate, on various datasets, that only a fraction of sensor information is relevant for human activity recognition.

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Flexible Measurement Testbed for Evaluating Time-Sensitive Networking in Industrial Automation Applications

Flexible Measurement Testbed for Evaluating Time-Sensitive N...

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IEEE Conference on Network Softwarization (NetSoft)

作者： Stefan Senk Marian Ulbricht Javier Acevedo Giang T. Nguyen Patrick Seeling Frank H. P. Fitzek Deutsche Telekom Chair of Communication Networks TU Dresden Haptic Communication Systems TU Dresden Centre for Tactile Internet with Human-in-the-Loop (CeTI) Department of Computer Science Central Michigan University

ISBN: (数字)9781665406949

ISBN: (纸本)9781665406956

Deterministic communications are required for industrial environments, yet their realization is a challenging task. Time-Sensitive Networking (TSN) is intended to enable deterministic communication over inexpensive Ethernet networks. Standardized by the IEEE TSN working group, TSN enables precise control of time synchronization, traffic shaping, reliability enhancements, and network administration to answer the demands of industrial control applications. Subsequently, there is a significant need to enable turnkey research and implementation efforts. However, a current lack of open-sourced testbed implementations to investigate and study the behavior of TSN network devices limits verification to simulation and theoretical models. We introduce a publicly available, flexible, and open-sourced measurement testbed for evaluating TSN in the context of industrial automation applications to address the need to perform real-world measurements. In this contribution, we describe our testbed combining Commercial-Off-The-Shelf (COTS) hardware and existing open-source tools as a platform for in-depth evaluation of TSN devices. Providing detailed TSN backgrounds, we describe an in-depth performance analysis for our implementation. For a common Tactile Internet scenario, we observe an accuracy of close to 5 ns achievable with our publicly available COTS setup.

关键词： Performance evaluation Automation Turnkey project Current measurement Hardware Behavioral sciences Software measurement

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Optimizing Edge SLAM: Judicious Parameter Settings and Parallelized Map Updates

Optimizing Edge SLAM: Judicious Parameter Settings and Paral...

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GLOBECOM 2022 - 2022 IEEE Global communications Conference

作者： Peter Sossalla Johannes Hofer Justus Rischke Johannes Busch Giang T. Nguyen Martin Reisslein Frank H. P. Fitzek Deutsche Telekom Chair of Communication Networks TU Dresden Germany Haptic Communication Systems TU Dresden Germany Centre for Tactile Internet with Human-in-the-Loop (CeTI) TU Dresden Germany Electrical Computer and Energy Eng. Arizona State Univ. Tempe AZ USA

ISBN: (纸本)9781665435413

Edge Simultaneous Localization and Mapping (SLAM) retains only the tracking on the mobile device, while offloading the compute-intensive local mapping and loop close to edge computing. Existing Edge SLAM approaches incur relatively high delays for offloading, resulting in high failure probabilities, i.e., low reliability, for commonly used public SLAM datasets. We discovered that two parameters which had not previously been studied in detail, namely the number of features and the number of keyframes that are bundled for a local map update, play a critical role in the offloading delay. Also, previous approaches updated the local map in the mobile device in a serial manner, incurring map update latencies. We study the numbers of features and bundled keyframes in detail and we parallelize the local map update. We find that judicious parameter settings, namely relatively small numbers of features (750 per frame) and bundled keyframes (1, i.e., effectively no bundling), reduce the map update latency to less than half compared to the previously common settings (1000 features per frame and 6 keyframes used for a map update). For a low network latency of 20ms, these judicious parameter settings in conjunction with our parallelized local map updating, reduce the 79% failure rate of the previous Edge SLAM systems down to 2%.

关键词： Tracking loops Simultaneous localization and mapping Mobile handsets Delays Reliability Global communication Edge computing

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Explainable Asymmetric Auto-Encoder for End-to-End Learning of IoBNT communications

Explainable Asymmetric Auto-Encoder for End-to-End Learning ...

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Machine Learning for communication and Networking (ICMLCN), IEEE International Conference on

作者： Roya Khanzadeh Stefan Angerbauer Jorge Torres Gomez Pit Hofmann Falko Dressler Frank H. P. Fitzek Andreas Springer Werner Haselmayr Institute for Communications Engineering and RF-Systems JKU LIT SAL eSPML Lab Johannes Kepler University Linz Linz Austria School of Electrical Engineering and Computer Science TU Berlin Berlin Germany Deutsche Telekom Chair of Communication Networks Technische Universität Dresden Dresden Germany Centre for Tactile Internet With Human-in-the-Loop (CeTI) Dresden Germany Institute for Communications Engineering and RF-Systems Johannes Kepler University Linz Linz Austria

ISBN: (数字)9798350343199

ISBN: (纸本)9798350343205

The Internet of Bio-Nano Things (IoBNT) is envisioned to be a heterogeneous network of artificial and natural units that are connected to the Internet. Hence, it extends the connectivity and control to unconventional domains, such as the human body. A potential use case for IoBNT is the communication from the outside to the inside of the human body. In this scenario, typically the Receiver (RX) inside the human body has limited computational complexity, while the Transmitter (TX) outside has large computational resources. In this paper, we address this scenario and propose a novel Asymmetric Auto-Encoder (AAEC) architecture for end-to-end learning of a Molecular communication (MC) system. It applies a Neural Network (NN) at the TX and a low-complexity slope detector at the RX. We discuss the different layers of the NN-based TX and the corresponding training approach. Moreover, we investigate the explainability of the NN-based TX and show through the use of meta modeling that it can be approximated by a linear model. In addition, we demonstrate that the proposed AAEC resembles an MC system with Zero Forcing (ZF) precoding for low and moderate Inter Symbol Interference (ISI). Finally, through numerical results, we confirmed the aforementioned findings and showed that the proposed AAEC outperforms MC systems with and without ZF precoding, especially in high ISI scenarios.

关键词： Training Transmitters Precoding Symbols Molecular communication Artificial neural networks Receivers

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Coordinated Multi-Agent Reinforcement Learning for Swarm Battery Control

Coordinated Multi-Agent Reinforcement Learning for Swarm Bat...

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IEEE Canadian Conference on Electrical and computer Engineering

作者： Niklas Ebell Marco Pruckner Energy Informatics Chair for Computer Networks and Communication Systems University of Erlangen-Nuremberg Germany

ISBN: (纸本)9781538624111

New ideas supporting the transition of the energy system are needed. To keep the electric grid in stable operation at times of high volatile supply from renewable energy sources one possibility is seen in distributed battery energy storage systems. They provide flexibility as a ancillary service for transmission system operators as well as improving self-sufficiency for residential buildings with photovoltaic systems. This research-in-progress paper presents an approach towards a coordinated multi-agent reinforcement learning-based swarm battery control. The goal is to use reinforcement learning to manage the battery's power flows between the battery, a photovoltaic system, a household's electric load and the electric grid. Our approach includes the use of the battery to offer frequency containment reserve as well as to improve energy self-sufficiency. As a last step, we compare the performance of our algorithm with a rule-based approach defining the same system configuration and objective.

关键词： Battery Energy Storage System Battery Control Reinforcement Learning multi-agent SWARM

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Interoperability and security challenges of Industrie 4.0 47

Interoperability and security challenges of Industrie 4.0

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47. Jahrestagung der Gesellschaft fur Informatik, Informatik 2017 - 47th Annual Meeting of the German Informatics Society (GI), Informatics 2017

作者： Watson, Venesa Tellabi, Asmaa Sassmannshausen, Jochen Lou, Xinxin University of Siegen Faculty of Science and Engineering Chair for Data Communication Systems Hölderlinstraße 3 Siegen57068 Germany Bielefeld University Department of Computer Networks and Distributed Systems Universitätsstraße 25 Bielefeld33615 Germany

ISBN: (纸本)9783885796695

Industrie 4.0 (I4.0) is the fourth industrial revolution, which will see the digital transformation of manufacturing, through the integration on Industrial Internet of Things (IIoT), Data and Services, and the convergence of Information Technology (IT) and Operational Technology (OT). With any such transformation, there exists challenges that must first be addressed for a successful outcome. For I4.0, these are interoperability and security, which respectively arise from the interconnection of devices from different manufacturers and/or with different communication protocols;and the interconnection of networks with competing critical and non-critical traffic, and the increased access to initially isolated networks. This paper presents an overview of standards, such as IEC 62443, the ISO 27000 series, IEC 62541 Open Connectivity Unified Architecture (OPC UA) and Time Sensitive networks (TSN) (IEEE 1722-2016), which addresses these challenges. © 2017 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Interoperability

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SWARM - Strategies for Providing Frequency Containment Reserve Power with a Distributed Battery Storage System

SWARM - Strategies for Providing Frequency Containment Reser...

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IEEE International Energy Conference

作者： David Steber Peter Bazan Reinhard German Computer Science 7 - Chair for Computer Networks and Communication Systems Friedrich-Alexander University Erlangen-Nurnberg

ISBN: (纸本)9781467384643

Virtual power plants can participate in energy markets. This paper presents the modeling approach used in the current research project SWARM for simulating the frequency containment reserve (FCR) power provision with a virtual mass storage. It participates in the German FCR market since July 2015 and is composed of battery energy storage systems (ESSs), which are in fact installed in households with roof-top PV plants and by this means increase the household's internal PV-converted power consumption and offer FCR power at the same time. The paper investigates the effects of more sever restrictions on FCR power provision as have recently been required by the German TSOs. These restrictions translate to relatively small bounds for the state of charge (SOC) of the batteries and have a significant influence on the household's profitability of installing an ESS.

关键词： Central controlled distributed batteries Frequency containment reserve power Internal PV-converted power consumption

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SWARM - increasing households’ internal PV consumption and offering primary control power with distributed batteries 4th

SWARM - increasing households’ internal PV consumption and ...

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4th D-A-CH Conference on Energy Informatics, EI 2015

作者： Steber, David Bazan, Peter German, Reinhard Chair of Computer Networks and Communication Systems Friedrich-Alexander-University Erlangen-Nürnberg Martensstr. 3 Erlangen91058 Germany

ISBN: (纸本)9783319258751

This Research in Progress Paper deals with a simulation approach for a virtual mass storage composed of small distributed battery energy storage units, installed in households with a roof-top photovoltaic system. On the one hand the household’s internal consumption of photovoltaic energy is maximized and on the other hand primary control reserve power is provided by a central storage controller. This concept is academically approved and rolled out in the field within this project. First simulation results show a household’s benefit of installing a battery energy storage system and an accurate working of the implemented virtual mass storage. © Springer International Publishing Switzerland 2015.

关键词： Power control

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Impact of intermediate layer on quality of experience of HTTP adaptive streaming

Impact of intermediate layer on quality of experience of HTT...

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International Conference on Network and Service Management

作者： Michael Seufert Tobias Hossfeld Christian Sieber Institute of Computer Science University of Würzburg Würzburg Germany Chair of Modeling of Adaptive Systems Universitat Duisburg-Essen Duisburg Nordrhein-Westfalen DE Institute for Communication Networks Technische Universität München Munich Germany

ISBN: (纸本)9781467378574

HTTP Adaptive Streaming (HAS) adapts the video quality to the current network condition by switching between different quality layers. As HAS was shown to perform better than classical video streaming, it is becoming increasingly popular. Recent research showed that quality switch amplitude and time on layer have an impact on the Quality of Experience (QoE) of HAS. However, those studies focused only on adaptation between two layers so far. This work extends these findings by taking adaptation between three layers into account. Thereby, especially the impact of an intermediate layer on user perceived quality is investigated. Crowdsourcing experiments were conducted in order to collect subjective ratings for adaptation between three layers. The results indicate that the quality of each layer and the time on each layer are important QoE parameters. This encourages the usage of temporal pooling approaches for QoE prediction and QoE-aware traffic management. Therefore, mean pooling of per-frame metrics will be applied and its performance will be validated with the subjective crowdsourcing results.

关键词： Crowdsourcing Switches Streaming media Adaptation models Video recording Quality assessment Measurement

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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