{"product_id":"wireless-sensor-networks-in-smart-environments-isbn-9781394249824","title":"Wireless Sensor Networks in Smart Environments","description":"\u003cp\u003e\u003cb\u003eUnderstand the fundamental building blocks of the Internet of Things\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eThe 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. \u003ci\u003eWireless Sensor Networks in Smart Environments\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eReaders will also find: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eConcrete real-world examples of recent applications\u003c\/li\u003e\n\u003cli\u003eDetailed discussion of WSNs from the perspectives of signal processing, data communication, and security\u003c\/li\u003e\n\u003cli\u003eCoverage of inference, learning, control, and decision-making processes\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eWireless Sensor Networks in Smart Environments\u003c\/i\u003e is ideal for researchers and graduate students working in signal processing, communications, and machine learning. \u003c\/p\u003e\u003cp\u003eAbout the Editors xvi\u003c\/p\u003e \u003cp\u003eList of Contributors xviii\u003c\/p\u003e \u003cp\u003ePreface xxiii\u003c\/p\u003e \u003cp\u003eAcknowledgments xxv\u003c\/p\u003e \u003cp\u003eIntroduction xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Signal Processing in Wireless Sensor Networks 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Graph Signal Processing in Wireless Sensor Networks 3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eGal Morgenstern, Lital Dabush, Morad Halihal, Tirza Routtenberg, and H. Vincent Poor\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 Graph Models for WSNs 4\u003c\/p\u003e \u003cp\u003e1.3 Concepts in GSP 8\u003c\/p\u003e \u003cp\u003e1.4 GSP-Based Smoothness Validation for WSN Signals 13\u003c\/p\u003e \u003cp\u003e1.5 GSP-Based Signal Recovery in WSN Models with Missing Data 17\u003c\/p\u003e \u003cp\u003e1.6 GSP-Based Anomaly Detection for WSN 20\u003c\/p\u003e \u003cp\u003e1.7 GSP-Based Graph Topology Identification for ModelingWSNs 23\u003c\/p\u003e \u003cp\u003e1.8 Conclusions and Future Directions 26\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Learning and Optimization in Wireless Sensor Networks 35\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMuhammad I. Qureshi, Apostolos I. Rikos, Themistoklis Charalambous, and Usman A. Khan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 35\u003c\/p\u003e \u003cp\u003e2.2 Notations and Definitions 38\u003c\/p\u003e \u003cp\u003e2.3 Problem Formulation 40\u003c\/p\u003e \u003cp\u003e2.4 Distributed Optimization Methods 41\u003c\/p\u003e \u003cp\u003e2.5 Extensions of DGD 44\u003c\/p\u003e \u003cp\u003e2.6 Distributed Fine-Tuning of Vision Transformers 57\u003c\/p\u003e \u003cp\u003e2.7 Discussion and Future Directions 58\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Distributed Non-Bayesian Quickest Change Detection with Energy Harvesting Sensors 65\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eEmma Green and Subhrakanti Dey\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 65\u003c\/p\u003e \u003cp\u003e3.2 System Model 66\u003c\/p\u003e \u003cp\u003e3.3 Quickest Change Detection at the FC 69\u003c\/p\u003e \u003cp\u003e3.4 Optimization Problem Formulation 70\u003c\/p\u003e \u003cp\u003e3.5 Detection Delay Analysis When H ≥ Es for the Distributed Scenario 72\u003c\/p\u003e \u003cp\u003e3.6 Simulation Results 78\u003c\/p\u003e \u003cp\u003e3.7 Conclusions and FutureWork 83\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Communications Technologies in Wireless Sensor Networks 87\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 RIS-Assisted Channel-Aware Decision Fusion 89\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDomenico Ciuonzo, Alessio Zappone, Pierluigi Salvo Rossi, and Marco Di Renzo\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 89\u003c\/p\u003e \u003cp\u003e4.2 System Model 91\u003c\/p\u003e \u003cp\u003e4.3 Combined Design of Fusion Rule and RIS 93\u003c\/p\u003e \u003cp\u003e4.4 Performance Analysis 98\u003c\/p\u003e \u003cp\u003e4.5 Conclusions and Further Reading 102\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Data Fusion in Millimeter Wave Massive MIMO Wireless Sensor Networks 107\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eApoorva Chawla, Domenico Ciuonzo, Aditya K. Jagannatham, and Pierluigi Salvo Rossi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 107\u003c\/p\u003e \u003cp\u003e5.2 System Model 109\u003c\/p\u003e \u003cp\u003e5.3 Problem Formulation 111\u003c\/p\u003e \u003cp\u003e5.4 Sensor Gain Optimization 115\u003c\/p\u003e \u003cp\u003e5.5 Power Scaling Laws 116\u003c\/p\u003e \u003cp\u003e5.6 SBL-Based CSI Estimation 118\u003c\/p\u003e \u003cp\u003e5.7 Simulation Results 122\u003c\/p\u003e \u003cp\u003e5.8 Conclusions 125\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Software-Defined Radio (SDR)-Based Real-Time WLANs for Industrial Wireless Sensing and Control 129\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eZelin Yun, Natong Lin, Shengli Zhou, and Song Han\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 129\u003c\/p\u003e \u003cp\u003e6.2 RT-WiFi Based on IEEE 802.11a\/g 132\u003c\/p\u003e \u003cp\u003e6.3 SRT-WiFi Based on IEEE 802.11a\/g 135\u003c\/p\u003e \u003cp\u003e6.4 GR-WiFi Based on 802.11a\/g\/n\/ac 146\u003c\/p\u003e \u003cp\u003e6.5 Conclusion and Future Work 153\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Cyber-Security in Wireless Sensor Networks 157\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Security and Privacy in Distributed Kalman Filtering 159\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNaveen K. D. Venkategowda, Ashkan Moradi, and Stefan Werner\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 159\u003c\/p\u003e \u003cp\u003e7.2 Distributed Kalman Filter 161\u003c\/p\u003e \u003cp\u003e7.3 Security in Distributed Kalman Filter 164\u003c\/p\u003e \u003cp\u003e7.4 Privacy in Distributed Kalman Filters 171\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Event-Triggered and Privacy-Preserving Anomaly Detection for Smart Environments 185\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eYasin Yilmaz, Mehmet Necip Kurt, and Xiaodong Wang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 185\u003c\/p\u003e \u003cp\u003e8.2 Background and Literature Review 186\u003c\/p\u003e \u003cp\u003e8.3 Event-Triggered Anomaly Detection 188\u003c\/p\u003e \u003cp\u003e8.4 Privacy-Preserving Anomaly Detection 194\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Decision-Making in Energy-Efficient Ordered Transmission-Based Networks Under Byzantine Attacks 209\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eChen Quan and Pramod K. Varshney\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 209\u003c\/p\u003e \u003cp\u003e9.2 Byzantine Attack Model 210\u003c\/p\u003e \u003cp\u003e9.3 COT-Based System 213\u003c\/p\u003e \u003cp\u003e9.4 CEOT-Based System 217\u003c\/p\u003e \u003cp\u003e9.5 Comparison of COT-Based and CEOT-Based Systems Under Attack 222\u003c\/p\u003e \u003cp\u003e9.6 Conclusion 227\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Applications in Smart Environments 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Internet of Musical Things for Smart Cities 233\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePaolo Casari and Luca Turchet\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 233\u003c\/p\u003e \u003cp\u003e10.2 Key-Enabling Technologies for IoMusT in Smart Musical Cities 236\u003c\/p\u003e \u003cp\u003e10.3 Smart Musical City Concept and Services 240\u003c\/p\u003e \u003cp\u003e10.4 Conclusions 245\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Robust Target Tracking in Sensor Networks with Measurement Outliers 253\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHongwei Wang, Hongbin Li, and Jun Fang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 253\u003c\/p\u003e \u003cp\u003e11.2 Problem Formulation 255\u003c\/p\u003e \u003cp\u003e11.3 Centralized Robust Target Tracking 258\u003c\/p\u003e \u003cp\u003e11.4 Decentralized Robust Target Tracking 261\u003c\/p\u003e \u003cp\u003e11.5 Numerical Examples 266\u003c\/p\u003e \u003cp\u003e11.6 Conclusion 270\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 A Federated Prototype-Based Model for IoT Systems: A Study Case for Leakage Detection in a Real Water Distribution Network 273\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDiego P. Sousa, José M. B. da Silva Jr, Charles C. Cavalcante, and Carlo Fischione\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 273\u003c\/p\u003e \u003cp\u003e12.2 Prototype-Based Learning 275\u003c\/p\u003e \u003cp\u003e12.3 Federated Learning 278\u003c\/p\u003e \u003cp\u003e12.4 Federated Prototype-Based Models 279\u003c\/p\u003e \u003cp\u003e12.5 Case Study:Water Distribution Network in Stockholm 282\u003c\/p\u003e \u003cp\u003e12.6 Results and Discussions 289\u003c\/p\u003e \u003cp\u003e12.7 Conclusions 294\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Multi-Agent Inverse Learning for Sensor Networks: Identifying Coordination in UAV Networks 299\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eLuke Snow and Vikram Krishnamurthy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 299\u003c\/p\u003e \u003cp\u003e13.2 Multi-Objective Optimization and Revealed Preferences 300\u003c\/p\u003e \u003cp\u003e13.3 Multi-Objective Optimization in UAV Networks 308\u003c\/p\u003e \u003cp\u003e13.4 Detection of Coordination 320\u003c\/p\u003e \u003cp\u003e13.5 Conclusion 324\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Immersive IoT Technologies for Smart Environments 327\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSubhas C. Mukhopadhyay, Anindya Nag, and Nagender K. Suryadevara\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 327\u003c\/p\u003e \u003cp\u003e14.2 State-of-the-Art 328\u003c\/p\u003e \u003cp\u003e14.3 Immersive Technologies 333\u003c\/p\u003e \u003cp\u003e14.4 Immersive IoT Technologies 336\u003c\/p\u003e \u003cp\u003e14.5 Network and Remote Execution Model 339\u003c\/p\u003e \u003cp\u003e14.6 Results 344\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Deployment of IoT in Smart Environments: Challenges and Experiences 353\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eWaltenegus Dargie, Michel Rottleuthner, Thomas C. Schmidt, and Matthias Wählisch\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 353\u003c\/p\u003e \u003cp\u003e15.2 Application Scenarios and Use Cases 356\u003c\/p\u003e \u003cp\u003e15.3 Requirements Analysis 367\u003c\/p\u003e \u003cp\u003e15.4 System Support 369\u003c\/p\u003e \u003cp\u003e15.5 Open Issues and Conclusions 372\u003c\/p\u003e \u003cp\u003eBibliography 372\u003c\/p\u003e \u003cp\u003eIndex 377\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eDomenico Ciuonzo, PhD, MSc,\u003c\/b\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePierluigi Salvo Rossi, PhD,\u003c\/b\u003e 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.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eUnderstand the fundamental building blocks of the Internet of Things\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eThe 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. \u003ci\u003eWireless Sensor Networks in Smart Environments\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eReaders will also find: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eConcrete real-world examples of recent applications\u003c\/li\u003e\n\u003cli\u003eDetailed discussion of WSNs from the perspectives of signal processing, data communication, and security\u003c\/li\u003e\n\u003cli\u003eCoverage of inference, learning, control, and decision-making processes\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eWireless Sensor Networks in Smart Environments\u003c\/i\u003e is ideal for researchers and graduate students working in signal processing, communications, and machine learning.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47990498787557,"sku":"NP9781394249824","price":135.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781394249824.jpg?v=1761788071","url":"https:\/\/k12savings.com\/products\/wireless-sensor-networks-in-smart-environments-isbn-9781394249824","provider":"K12savings","version":"1.0","type":"link"}