Del i serien Control, Robotics and Sensors

Data Fusion in Wireless Sensor Networks

A statistical signal processing perspective

Inbunden, Engelska, 2019

AvDomenico, Ciuonzo,Domenico Ciuonzo,Pierluigi Salvo Rossi

2 109 kr

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The role of data fusion has been expanding in recent years through the incorporation of pervasive applications, where the physical infrastructure is coupled with information and communication technologies, such as wireless sensor networks for the internet of things (IoT), e-health and Industry 4.0. In this edited reference, the authors provide advanced tools for the design, analysis and implementation of inference algorithms in wireless sensor networks.The book is directed at the sensing, signal processing, and ICTs research communities. The contents will be of particular use to researchers (from academia and industry) and practitioners working in wireless sensor networks, IoT, E-health and Industry 4.0 applications who wish to understand the basics of inference problems. It will also be of interest to professionals, and graduate and PhD students who wish to understand the fundamental concepts of inference algorithms based on intelligent and energy-efficient protocols.

Produktinformation

Utgivningsdatum2019-05-06
Mått156 x 234 x 23 mm
Vikt718 g
FormatInbunden
SpråkEngelska
SerieControl, Robotics and Sensors
Antal sidor352
FörlagInstitution of Engineering and Technology
ISBN9781785615849

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Domenico Ciuonzo was a Researcher at NM-2 s.r.l., Naples, during 2017-18. He is now an Assistant Professor at University of Naples Federico II.Pierluigi Salvo Rossi is Principal Engineer with the Department of Advanced Analytics and Machine Learning, Kongsberg Digital AS, Norway. He is an IEEE Senior Member, Associate Editor of IEEE Transactions on Wireless Communications, and Senior Editor of IEEE Communications Letters.

Part I: Sensing model uncertaintyChapter 1: Generalized score-tests for decision fusion with sensing model uncertaintyChapter 2: Compressed distributed detection and estimationChapter 3: Heterogeneous sensor data fusion by deep learning Part II: Reporting channel uncertaintyChapter 4: Energy-efficient clustering and collision-aware distributed detection/estimation in random-access-based WSNsChapter 5: Channel-aware decision fusion in MIMO wireless sensor networksChapter 6: Channel-aware detection and estimation in the massive MIMO regime Part III: Distributed inference over graphsChapter 7: Decentralized detection via running consensusChapter 8: Distributed recursive testing of composite hypothesis in multi-agent networksChapter 9: Expectation-maximisation based distributed estimation in sensor networks Part IV: Cross-layer issuesChapter 10: Distributed estimation in energy harvesting wireless sensor networksChapter 11: Secure estimation in wireless sensor networks in the presence of an eavesdropperChapter 12: Robust fusion of unreliable data sources using error-correcting output codesChapter 13: Conclusions and future perspectives