Data Fusion in Wireless Sensor Networks
A statistical signal processing perspective
Published on 6. May 2019
Book
Hardback
352 pages
978-1-78561-584-9 (ISBN)
Description
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.
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.
More details
Series
Language
English
Place of publication
Stevenage
United Kingdom
Target group
College/higher education
Professional and scholarly
Dimensions
Height: 236 mm
Width: 163 mm
Thickness: 23 mm
Weight
680 gr
ISBN-13
978-1-78561-584-9 (9781785615849)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
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.
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.
Content
Part I: Sensing model uncertainty
Chapter 1: Generalized score-tests for decision fusion with sensing model uncertainty
Chapter 2: Compressed distributed detection and estimation
Chapter 3: Heterogeneous sensor data fusion by deep learning
Part II: Reporting channel uncertainty
Chapter 4: Energy-efficient clustering and collision-aware distributed detection/estimation in random-access-based WSNs
Chapter 5: Channel-aware decision fusion in MIMO wireless sensor networks
Chapter 6: Channel-aware detection and estimation in the massive MIMO regime
Part III: Distributed inference over graphs
Chapter 7: Decentralized detection via running consensus
Chapter 8: Distributed recursive testing of composite hypothesis in multi-agent networks
Chapter 9: Expectation-maximisation based distributed estimation in sensor networks
Part IV: Cross-layer issues
Chapter 10: Distributed estimation in energy harvesting wireless sensor networks
Chapter 11: Secure estimation in wireless sensor networks in the presence of an eavesdropper
Chapter 12: Robust fusion of unreliable data sources using error-correcting output codes
Chapter 13: Conclusions and future perspectives
Chapter 1: Generalized score-tests for decision fusion with sensing model uncertainty
Chapter 2: Compressed distributed detection and estimation
Chapter 3: Heterogeneous sensor data fusion by deep learning
Part II: Reporting channel uncertainty
Chapter 4: Energy-efficient clustering and collision-aware distributed detection/estimation in random-access-based WSNs
Chapter 5: Channel-aware decision fusion in MIMO wireless sensor networks
Chapter 6: Channel-aware detection and estimation in the massive MIMO regime
Part III: Distributed inference over graphs
Chapter 7: Decentralized detection via running consensus
Chapter 8: Distributed recursive testing of composite hypothesis in multi-agent networks
Chapter 9: Expectation-maximisation based distributed estimation in sensor networks
Part IV: Cross-layer issues
Chapter 10: Distributed estimation in energy harvesting wireless sensor networks
Chapter 11: Secure estimation in wireless sensor networks in the presence of an eavesdropper
Chapter 12: Robust fusion of unreliable data sources using error-correcting output codes
Chapter 13: Conclusions and future perspectives