Understand the fundamental building blocks of the Internet of Things The Internet of Things is the term for an ever-growing body of physical devices, vehicles, rooms, and other objects that can collect and exchange data using embedded capacities for network connectivity. Wireless Sensor Networks (WSNs) represent the ‘sensing arm’ of this network of objects, providing the mechanism for collecting and transmitting data from these objects. Wireless Sensor Networks in Smart Environments offers a timely and comprehensive overview of these networks and their broader impacts. Adopting both methodology- and application-oriented perspectives, the book covers both the foundational principles of WSNs and the most recent technological developments. Readers will also find: Concrete real-world examples of recent applications Detailed discussion of WSNs from the perspectives of signal processing, data communication, and security Coverage of inference, learning, control, and decision-making processes Wireless Sensor Networks in Smart Environments is ideal for researchers and graduate students working in signal processing, communications, and machine learning.

Table of Contents:
About the Editors xvi List of Contributors xviii Preface xxiii Acknowledgments xxv Introduction xxvii Part I Signal Processing in Wireless Sensor Networks 1 1 Graph Signal Processing in Wireless Sensor Networks 3 Gal Morgenstern, Lital Dabush, Morad Halihal, Tirza Routtenberg, and H. Vincent Poor 1.1 Introduction 3 1.2 Graph Models for WSNs 4 1.2.1 Distance-Based Model 5 1.2.2 Correlation-Based Model 6 1.2.3 Alternative Models 7 1.3 Concepts in GSP 8 1.3.1 Graph Spectrum 9 1.3.2 Graph Signal Properties 9 1.3.3 Graph Filters 10 1.4 GSP-Based Smoothness Validation for WSN Signals 13 1.4.1 Smooth Graph Filters 13 1.4.2 Semi-parametric Graph Signal Smoothness Detector 15 1.5 GSP-Based Signal Recovery in WSN Models with Missing Data 17 1.5.1 Signal Recovery Approaches 18 1.5.2 GSP-Based Sampling Policies 19 1.6 GSP-Based Anomaly Detection for WSN 20 1.6.1 Hypothesis Testing Problem 21 1.6.2 Graph High-Pass Filter (GHPF)-Based Detection 21 1.6.3 Illustrative Example 22 1.7 GSP-Based Graph Topology Identification for ModelingWSNs 23 1.7.1 ML Estimation of the Graph Laplacian Matrix 23 1.7.2 Topology Change Identification 24 1.8 Conclusions and Future Directions 26 Acknowledgments 28 Bibliography 28 2 Learning and Optimization in Wireless Sensor Networks 35 Muhammad I. Qureshi, Apostolos I. Rikos, Themistoklis Charalambous, and Usman A. Khan 2.1 Introduction 35 2.1.1 RelatedWork 37 2.2 Notations and Definitions 38 2.2.1 Graph-Theoretic Notions 39 2.2.2 Summary of Variables 39 2.3 Problem Formulation 40 2.4 Distributed Optimization Methods 41 2.4.1 Distributed Gradient Descent 42 2.5 Extensions of DGD 44 2.5.1 Extension to Directed Communication 44 2.5.2 Operation Over Wireless Networks 46 2.5.2.1 Quantized Communication 47 2.5.2.2 Distributed Gradient Descent with Quantized Communication 47 2.5.2.3 Enhancing Accuracy of Optimal Solution 51 2.5.3 Stochastic Implementation 54 2.6 Distributed Fine-Tuning of Vision Transformers 57 2.7 Discussion and Future Directions 58 Acknowledgments 59 Bibliography 59 3 Distributed Non-Bayesian Quickest Change Detection with Energy Harvesting Sensors 65 Emma Green and Subhrakanti Dey 3.1 Introduction 65 3.2 System Model 66 3.2.1 Decentralized Detection Scenario 66 3.2.2 Distributed Detection Scenario 68 3.3 Quickest Change Detection at the FC 69 3.4 Optimization Problem Formulation 70 3.4.1 Optimal Threshold Quantization 71 3.5 Detection Delay Analysis When H ≥ Es for the Distributed Scenario 72 3.5.1 Average Detection Delay 74 3.5.1.1 Average Detection Delay for Distributed Change Detection with Local Detection at the Sensors 75 3.5.2 Asymptotic Distribution of the First Passage Time to a False Alarm 76 3.5.2.1 Asymptotic Distribution of First-Passage Time to False Alarm for Distributed Change Detection with Local Detection at the Sensors 76 3.5.2.2 Average First-Passage Time to False Alarm for Distributed Change Detection with Local Detection at the Sensors 77 3.6 Simulation Results 78 3.6.1 Decentralized Detection Results 78 3.6.2 Distributed Detection Results 81 3.7 Conclusions and FutureWork 83 Bibliography 84 Part II Communications Technologies in Wireless Sensor Networks 87 4 RIS-Assisted Channel-Aware Decision Fusion 89 Domenico Ciuonzo, Alessio Zappone, Pierluigi Salvo Rossi, and Marco Di Renzo 4.1 Introduction 89 4.2 System Model 91 4.3 Combined Design of Fusion Rule and RIS 93 4.4 Performance Analysis 98 4.5 Conclusions and Further Reading 102 Acknowledgments 103 Bibliography 103 5 Data Fusion in Millimeter Wave Massive MIMO Wireless Sensor Networks 107 Apoorva Chawla, Domenico Ciuonzo, Aditya K. Jagannatham, and Pierluigi Salvo Rossi 5.1 Introduction 107 5.2 System Model 109 5.2.1 C-MIMO System 109 5.2.2 D-MIMO System 110 5.3 Problem Formulation 111 5.3.1 C-MIMO: Fusion Rule for Perfect CSI 111 5.3.2 D-MIMO: Fusion Rule for Perfect CSI 113 5.4 Sensor Gain Optimization 115 5.4.1 Optimized Sensor Gains for C-MIMO 115 5.4.2 Optimized Sensor Gains for D-MIMO 116 5.5 Power Scaling Laws 116 5.5.1 Uniform Transmit Gains 117 5.5.2 Optimal Transmit Gains 117 5.6 SBL-Based CSI Estimation 118 5.6.1 C-MIMO: Fusion Rule for Imperfect CSI 119 5.6.2 D-MIMO: Fusion Rule for Imperfect CSI 121 5.7 Simulation Results 122 5.8 Conclusions 125 Bibliography 125 6 Software-Defined Radio (SDR)-Based Real-Time WLANs for Industrial Wireless Sensing and Control 129 Zelin Yun, Natong Lin, Shengli Zhou, and Song Han 6.1 Introduction 129 6.2 RT-WiFi Based on IEEE 802.11a/g 132 6.2.1 RT-WiFi Protocol Design 132 6.2.2 Performance Evaluation 134 6.3 SRT-WiFi Based on IEEE 802.11a/g 135 6.3.1 Programmable Logic (PL) in SRT-WiFi 137 6.3.1.1 TDMA Block Design in SRT-WiFi PL 137 6.3.1.2 TDMA Time Synchronization Design 138 6.3.1.3 Queue Management 139 6.3.1.4 Link Quality Measurement 142 6.3.2 Processing System (PS) in SRT-WiFi 143 6.3.3 Performance Evaluation 144 6.4 GR-WiFi Based on 802.11a/g/n/ac 146 6.4.1 Packet Transmission Design 146 6.4.2 Packet Reception Design 147 6.4.3 Implementation and Evaluation 148 6.4.3.1 Key Blocks in GR-WiFi Implementation 148 6.4.3.2 Performance Evaluation 151 6.5 Conclusion and Future Work 153 Bibliography 154 Part III Cyber-Security in Wireless Sensor Networks 157 7 Security and Privacy in Distributed Kalman Filtering 159 Naveen K. D. Venkategowda, Ashkan Moradi, and Stefan Werner 7.1 Introduction 159 7.2 Distributed Kalman Filter 161 7.3 Security in Distributed Kalman Filter 164 7.3.1 Byzantine Robust Distributed Kalman Filter 165 7.3.2 Performance Analysis 167 7.4 Privacy in Distributed Kalman Filters 171 7.4.1 Privacy Measures 171 7.4.2 Privacy-Preserving Distributed Kalman Filter 172 7.4.3 Privacy Guarantees 175 7.4.4 Simulation Results 177 Bibliography 180 8 Event-Triggered and Privacy-Preserving Anomaly Detection for Smart Environments 185 Yasin Yilmaz, Mehmet Necip Kurt, and Xiaodong Wang 8.1 Introduction 185 8.2 Background and Literature Review 186 8.3 Event-Triggered Anomaly Detection 188 8.3.1 Event Definitions at Nodes 190 8.3.2 Parametric Processing at Network Center 191 8.3.3 Nonparametric Processing at Network Center 192 8.4 Privacy-Preserving Anomaly Detection 194 8.4.1 Online Network Anomaly Detection 196 8.4.2 Experimental Results 199 8.4.3 DP Techniques 200 8.4.4 Anomaly Detection Performance 201 8.4.5 Differentially Private Event-Triggered Anomaly Detection 201 Bibliography 202 9 Decision-Making in Energy-Efficient Ordered Transmission-Based Networks Under Byzantine Attacks 209 Chen Quan and Pramod K. Varshney 9.1 Introduction 209 9.2 Byzantine Attack Model 210 9.2.1 Typical Attack Model inWSNs 211 9.2.2 Existing Defense Schemes 212 9.3 COT-Based System 213 9.3.1 System Model of COT-Based System 213 9.3.1.1 Attack Model 214 9.3.2 Performance Analysis 214 9.3.2.1 Detection Performance 215 9.3.2.2 Average Number of Transmissions Saved Under OA-Byzantine Attacks 215 9.4 CEOT-Based System 217 9.4.1 Attack Model 217 9.4.2 CEOT-Based System with DF-Byzantines 218 9.4.2.1 Detection Performance 218 9.4.2.2 Average Number of Transmissions Saved Under DF-Byzantine Attacks 219 9.4.3 CEOT-Based System with OA-Byzantines 220 9.4.3.1 Detection Performance 220 9.4.3.2 Average Number of Transmissions Saved Under OA-Byzantine Attacks 220 9.5 Comparison of COT-Based and CEOT-Based Systems Under Attack 222 9.5.1 Effect of OA-Byzantine Attacks on the COT-Based and CEOT-Based Systems 222 9.5.2 Effect of DF-Byzantine Attacks on the CEOT-Based System 224 9.5.3 Discussion 227 9.6 Conclusion 227 Bibliography 228 Part IV Applications in Smart Environments 231 10 Internet of Musical Things for Smart Cities 233 Paolo Casari and Luca Turchet 10.1 Introduction 233 10.2 Key-Enabling Technologies for IoMusT in Smart Musical Cities 236 10.2.1 Musical Things 236 10.2.2 5G-and-Beyond Networks 237 10.2.3 Datasets and Storage 239 10.3 Smart Musical City Concept and Services 240 10.3.1 Interaction Between Musicians and Virtual Agents on Server 240 10.3.2 Participatory Networked Music Performances 241 10.3.3 Cultural Heritage 242 10.3.4 Pedagogy 244 10.4 Conclusions 245 Bibliography 246 11 Robust Target Tracking in Sensor Networks with Measurement Outliers 253 Hongwei Wang, Hongbin Li, and Jun Fang 11.1 Introduction 253 11.2 Problem Formulation 255 11.2.1 Cubature Information Filter 257 11.3 Centralized Robust Target Tracking 258 11.4 Decentralized Robust Target Tracking 261 11.4.1 Consensus Strategy 261 11.4.2 Consensus on Prior 262 11.4.3 Consensus on Likelihood 263 11.4.4 Fusing the Consensus Results 264 11.5 Numerical Examples 266 11.6 Conclusion 270 Bibliography 270 12 A Federated Prototype-Based Model for IoT Systems: A Study Case for Leakage Detection in a Real Water Distribution Network 273 Diego P. Sousa, José M. B. da Silva Jr, Charles C. Cavalcante, and Carlo Fischione 12.1 Introduction 273 12.2 Prototype-Based Learning 275 12.2.1 Unsupervised Learning 276 12.2.2 Supervised Learning 277 12.3 Federated Learning 278 12.4 Federated Prototype-Based Models 279 12.5 Case Study:Water Distribution Network in Stockholm 282 12.5.1 Dataset Description 282 12.5.2 Feature Extraction 288 12.5.3 Dataset Settings 288 12.6 Results and Discussions 289 12.6.1 Numerical Results 289 12.6.2 Validation of the Canonical Discrimination Function 290 12.6.3 Minimization of the Cost Function 291 12.6.4 Analysis of the Clustering Performance 292 12.6.5 Analysis of the Voronoi Regions 293 12.7 Conclusions 294 Acknowledgments 295 Bibliography 295 13 Multi-Agent Inverse Learning for Sensor Networks: Identifying Coordination in UAV Networks 299 Luke Snow and Vikram Krishnamurthy 13.1 Introduction 299 13.2 Multi-Objective Optimization and Revealed Preferences 300 13.2.1 Multi-Objective Optimization 300 13.2.1.1 Multi-Objective Problem 300 13.2.1.2 Multi-Objective Solution Concept 301 13.2.2 Inverse Multi-Objective Optimization 301 13.2.2.1 Inverse Multi-Objective Problem 301 13.2.2.2 Revealed Preferences 301 13.2.3 Outline 302 13.2.4 Multi-Objective Optimization 302 13.2.4.1 Multi-Objective Problem 302 13.2.4.2 Multi-Objective Solution Concept: Pareto Optimality 303 13.2.4.3 Computing Pareto Optimal Solutions 304 13.2.5 Inverse Multi-Objective Optimization 305 13.2.5.1 Inverse Multi-Objective Problem 305 13.2.5.2 Group Revealed Preferences 306 13.3 Multi-Objective Optimization in UAV Networks 308 13.3.1 Interaction Dynamics 309 13.3.2 UAV Network Coordination: Constrained Spectral Optimization 311 13.3.2.1 UAV Network Coordination 311 13.3.2.2 Multi-Target Spectral Dynamics 312 13.3.3 Multi-Target Filtering 314 13.3.3.1 Decoupled Kalman Filtering 314 13.3.3.2 Joint Probabilistic Data Association Filter 316 13.4 Detection of Coordination 320 13.4.1 Deterministic Coordination Detection 320 13.4.1.1 Numerical Example 321 13.4.2 Statistical Detection of Coordination 321 13.5 Conclusion 324 Bibliography 325 14 Immersive IoT Technologies for Smart Environments 327 Subhas C. Mukhopadhyay, Anindya Nag, and Nagender K. Suryadevara 14.1 Introduction 327 14.2 State-of-the-Art 328 14.3 Immersive Technologies 333 14.3.1 Augmented Reality (AR)/Virtual Reality (VR) and Mixed Reality (MR) 334 14.3.2 Smart Environments 335 14.4 Immersive IoT Technologies 336 14.4.1 System Model 338 14.5 Network and Remote Execution Model 339 14.5.1 Decision-Making Procedure 340 14.5.2 Data Collection 341 14.5.3 Optimal Problem Formulation 342 14.6 Results 344 References 348 15 Deployment of IoT in Smart Environments: Challenges and Experiences 353 Waltenegus Dargie, Michel Rottleuthner, Thomas C. Schmidt, and Matthias Wählisch 15.1 Introduction 353 15.2 Application Scenarios and Use Cases 356 15.2.1 Water Quality Monitoring 356 15.2.1.1 Challenges of Autonomous Mobile Sensing 356 15.2.1.2 System Architecture and Implementation 359 15.2.1.3 Deployment Results and Lessons Learned 360 15.2.2 Mobile Urban Sensing: Energy-Neutral Air Quality Monitoring 362 15.2.2.1 Challenges of Autonomous Mobile Sensing 363 15.2.2.2 System Architecture and Implementation 363 15.2.2.3 Deployment Results and Lessons Learned 364 15.3 Requirements Analysis 367 15.4 System Support 369 15.4.1 IoT Operating Systems 369 15.4.2 Smart City Infrastructure 370 15.5 Open Issues and Conclusions 372 Bibliography 372 Index 377