Ciuonzo D., Rossi P.S. (Eds.) Data Fusion in Wireless Sensor Networks: A statistical signal processing

The Institution of Engineering and Technology, 2019. — 349. — (IET Control, Robotics and Sensors Series 117). — ISBN: 978-1-78561-584-9.The Internet-of-Things revolution is coming to reality and most of real-life scenarios are experiencing the pervasive presence of network-enabled devices, in most cases sensors. The deployment of heterogeneous sensors at several locations enables collection of different kinds of (big amount of) data about the surrounding scenario. A wireless sensor network is the typical solution for data collection, data processing, and inference in most of Internet-of-Things applications.
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-healtah 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.Sensing model uncertainty
Generalized score-tests for decision fusion with sensing model uncertainty
Compressed distributed detection and estimation
Heterogeneous sensor data fusion by deep learning
Reporting channel uncertainty
Energy-efficient clustering and collision-aware distributed detection/estimation in random-access-based WSNs
Channel-aware decision fusion in MIMO wireless sensor networks
Channel-aware detection and estimation in the massive MIMO regime
Distributed inference over graphs
Decentralized detection via running consensus
Distributed recursive testing of composite hypothesis inmulti-agent networks
Expectation–maximisation based distributed estimation in sensor networks
Cross-layer issues
Distributed estimation in energy harvesting wireless sensor networks
Secure estimation in wireless sensor networks in the presence of an eavesdropper
Robust fusion of unreliable data sources using error-correcting output codes
Conclusions and future perspectives