This book describes the advanced tools required to design state-of-the-art inference algorithms for inference in wireless sensor networks. Written for the signal processing, communications, sensors and information fusion research communities, it covers the emerging area of data fusion in wireless sensor networks.
This book describes the advanced tools required to design state-of-the-art inference algorithms for inference in wireless sensor networks. Written for the signal processing, communications, sensors and information fusion research communities, it covers the emerging area of data fusion in wireless sensor networks.
1. 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 2. 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 3. 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 4. 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
1. 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 2. 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 3. 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 4. 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
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