Domenico Ciuonzo, PhD, MSc, is a Tenure-Track Professor at the Department of Electrical Engineering and Information Technologies, University of Naples, Federico II, Italy. He obtained his MSc and PhD in Computer Engineering from the University of Campania “L. Vanvitelli”, Italy, in 2009 and 2013, respectively. He was the recipient of two Best Paper awards (IEEE ICCCS 2019 and Elsevier Computer Networks 2020), the 2019 Exceptional Service Award from IEEE AESS, 2020 Early-Career Technical Achievement Award from IEEE SENSORS COUNCIL for sensor networks/systems and the 2021 Early-Career Award from IEEE AESS for contributions to decentralized inference and sensor fusion in networked sensor systems. Pierluigi Salvo Rossi, PhD, is a Full Professor and the Deputy Head with the Department of Electronic Systems, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. He is also a part-time Senior Research Scientist with the Department of Gas Technology, SINTEF Energy Research, Norway. Previously, he worked with Kongsberg Digital AS, Norway, with NTNU, Norway, with the Second University of Naples, Italy, and with the University of Naples “Federico II,” Italy. He held visiting appointments with Uppsala University, Sweden, with NTNU, Norway, with Lund University, Sweden, and with Drexel University, USA. He received his MSc in Telecommunications Engineering and PhD in Computer Engineering from the University of Naples “Federico II” in 2002 and 2005, respectively.

• About the Editors xviList of Contributors xviiiPreface xxiiiAcknowledgments xxvIntroduction xxviiPart I Signal Processing in Wireless Sensor Networks 11 Graph Signal Processing in Wireless Sensor Networks 3Gal Morgenstern, Lital Dabush, Morad Halihal, Tirza Routtenberg, and H. Vincent Poor1.1 Introduction 31.2 Graph Models for WSNs 41.3 Concepts in GSP 81.4 GSP-Based Smoothness Validation for WSN Signals 131.5 GSP-Based Signal Recovery in WSN Models with Missing Data 171.6 GSP-Based Anomaly Detection for WSN 201.7 GSP-Based Graph Topology Identification for ModelingWSNs 231.8 Conclusions and Future Directions 262 Learning and Optimization in Wireless Sensor Networks 35Muhammad I. Qureshi, Apostolos I. Rikos, Themistoklis Charalambous, and Usman A. Khan2.1 Introduction 352.2 Notations and Definitions 382.3 Problem Formulation 402.4 Distributed Optimization Methods 412.5 Extensions of DGD 442.6 Distributed Fine-Tuning of Vision Transformers 572.7 Discussion and Future Directions 583 Distributed Non-Bayesian Quickest Change Detection with Energy Harvesting Sensors 65Emma Green and Subhrakanti Dey3.1 Introduction 653.2 System Model 663.3 Quickest Change Detection at the FC 693.4 Optimization Problem Formulation 703.5 Detection Delay Analysis When H ≥ Es for the Distributed Scenario 723.6 Simulation Results 783.7 Conclusions and FutureWork 83Part II Communications Technologies in Wireless Sensor Networks 874 RIS-Assisted Channel-Aware Decision Fusion 89Domenico Ciuonzo, Alessio Zappone, Pierluigi Salvo Rossi, and Marco Di Renzo4.1 Introduction 894.2 System Model 914.3 Combined Design of Fusion Rule and RIS 934.4 Performance Analysis 984.5 Conclusions and Further Reading 1025 Data Fusion in Millimeter Wave Massive MIMO Wireless Sensor Networks 107Apoorva Chawla, Domenico Ciuonzo, Aditya K. Jagannatham, and Pierluigi Salvo Rossi5.1 Introduction 1075.2 System Model 1095.3 Problem Formulation 1115.4 Sensor Gain Optimization 1155.5 Power Scaling Laws 1165.6 SBL-Based CSI Estimation 1185.7 Simulation Results 1225.8 Conclusions 1256 Software-Defined Radio (SDR)-Based Real-Time WLANs for Industrial Wireless Sensing and Control 129Zelin Yun, Natong Lin, Shengli Zhou, and Song Han6.1 Introduction 1296.2 RT-WiFi Based on IEEE 802.11a/g 1326.3 SRT-WiFi Based on IEEE 802.11a/g 1356.4 GR-WiFi Based on 802.11a/g/n/ac 1466.5 Conclusion and Future Work 153Part III Cyber-Security in Wireless Sensor Networks 1577 Security and Privacy in Distributed Kalman Filtering 159Naveen K. D. Venkategowda, Ashkan Moradi, and Stefan Werner7.1 Introduction 1597.2 Distributed Kalman Filter 1617.3 Security in Distributed Kalman Filter 1647.4 Privacy in Distributed Kalman Filters 1718 Event-Triggered and Privacy-Preserving Anomaly Detection for Smart Environments 185Yasin Yilmaz, Mehmet Necip Kurt, and Xiaodong Wang8.1 Introduction 1858.2 Background and Literature Review 1868.3 Event-Triggered Anomaly Detection 1888.4 Privacy-Preserving Anomaly Detection 1949 Decision-Making in Energy-Efficient Ordered Transmission-Based Networks Under Byzantine Attacks 209Chen Quan and Pramod K. Varshney9.1 Introduction 2099.2 Byzantine Attack Model 2109.3 COT-Based System 2139.4 CEOT-Based System 2179.5 Comparison of COT-Based and CEOT-Based Systems Under Attack 2229.6 Conclusion 227Part IV Applications in Smart Environments 23110 Internet of Musical Things for Smart Cities 233Paolo Casari and Luca Turchet10.1 Introduction 23310.2 Key-Enabling Technologies for IoMusT in Smart Musical Cities 23610.3 Smart Musical City Concept and Services 24010.4 Conclusions 24511 Robust Target Tracking in Sensor Networks with Measurement Outliers 253Hongwei Wang, Hongbin Li, and Jun Fang11.1 Introduction 25311.2 Problem Formulation 25511.3 Centralized Robust Target Tracking 25811.4 Decentralized Robust Target Tracking 26111.5 Numerical Examples 26611.6 Conclusion 27012 A Federated Prototype-Based Model for IoT Systems: A Study Case for Leakage Detection in a Real Water Distribution Network 273Diego P. Sousa, José M. B. da Silva Jr, Charles C. Cavalcante, and Carlo Fischione12.1 Introduction 27312.2 Prototype-Based Learning 27512.3 Federated Learning 27812.4 Federated Prototype-Based Models 27912.5 Case Study:Water Distribution Network in Stockholm 28212.6 Results and Discussions 28912.7 Conclusions 29413 Multi-Agent Inverse Learning for Sensor Networks: Identifying Coordination in UAV Networks 299Luke Snow and Vikram Krishnamurthy13.1 Introduction 29913.2 Multi-Objective Optimization and Revealed Preferences 30013.3 Multi-Objective Optimization in UAV Networks 30813.4 Detection of Coordination 32013.5 Conclusion 32414 Immersive IoT Technologies for Smart Environments 327Subhas C. Mukhopadhyay, Anindya Nag, and Nagender K. Suryadevara14.1 Introduction 32714.2 State-of-the-Art 32814.3 Immersive Technologies 33314.4 Immersive IoT Technologies 33614.5 Network and Remote Execution Model 33914.6 Results 34415 Deployment of IoT in Smart Environments: Challenges and Experiences 353Waltenegus Dargie, Michel Rottleuthner, Thomas C. Schmidt, and Matthias Wählisch15.1 Introduction 35315.2 Application Scenarios and Use Cases 35615.3 Requirements Analysis 36715.4 System Support 36915.5 Open Issues and Conclusions 372Bibliography 372Index 377