Intelligent Sensor Networks The Integration of Sensor Networks, Signal Processing and Machine Learning

Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning.Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, including compressive sensing and sampling, distributed signal processing, and intelligent signal learning. Presenting recent research results of world-renowned sensing experts, the book is organized into three parts: Machine Learning—describes the application of machine learning and other AI principles in sensor network intelligence—covering smart sensor/transducer architecture and data representation for intelligent sensors Signal Processing—considers the optimization of sensor network performance based on digital signal processing techniques—including cross-layer integration of routing and application-specific signal processing as well as on-board image processing in wireless multimedia sensor networks for intelligent transportation systems Networking—focuses on network protocol design in order to achieve an intelligent sensor networking—covering energy-efficient opportunistic routing protocols for sensor networking and multi-agent-driven wireless sensor cooperation Maintaining a focus on "intelligent" designs, the book details signal processing principles in sensor networks. It elaborates on critical platforms for intelligent sensor networks and illustrates key applications—including target tracking, object identification, and structural health monitoring. It also includes a paradigm for validating the extent of spatiotemporal associations among data sources to enhance data cleaning in sensor networks, a sensor stream reduction application, and also considers the use of Kalman filters for attack detection in a water system sensor network that consists of water level sensors and velocity sensors.

INTELLIGENT SENSOR NETWORKS: MACHINE LEARNING APPROACHMachine Learning Basics; Krasimira Kapitanova and Sang H. SonModeling Unreliable Data and Sensors: Using Event Log Performance and F-Measure Attribute Selection; Vasanth Iyer, S. Sitharama Iyengar, and Srinivas SrivathsanIntelligent Sensor Interfaces and Data Format; Konstantin Mikhaylov, Joni Jamsa, Mika Luimula, Jouni Tervonen, and Ville AutioSmart Wireless Sensor Nodes for Structural Health Monitoring; Xuefeng Liu, Shaojie Tang, and Xiaohua XuKnowledge Representation and Reasoning for the Design of Resilient Sensor Networks; David W. Kelle, Touria El-Mezyani, Sanjeev K. Srivastava, and David A. Cartes Intelligent Sensor-to-Mission Assignment; Hosam RowaihyPrediction-Based Data Collection in Wireless Sensor Networks; Jann-Aël Le Borgne and Gianluca BontempiNeuro-Disorder Patient Monitoring via Gait Sensor Networks: Toward an Intelligent, Context-Oriented Signal Processing; Fei Hu, Qingquan Sun, and Qi HaoCognitive Wireless Sensor Networks; Sumit Kumar, Deepti Singhal, and Rama Murthy Garimella INTELLIGENT SENSOR NETWORKS: SIGNAL PROCESSINGRouting for Signal Processing; Wanzhi Qiu and Efstratios SkafidasOn-Board Image Processing in Wireless Multimedia Sensor Networks: A Parking Space Monitoring Solution for Intelligent Transportation Systems; Claudio Salvadori, Matteo Petracca, Marco Ghibaudi, and Paolo PaganoSignal Processing for Sensing and Monitoring Civil Infrastructure Systems; Mustafa Gul and F. Necati CatbasData Cleaning in Low Powered Wireless Sensor Networks; Qutub Ali Bakhtiar, Niki Pissinou, and Kia MakkiSensor Stream Reduction; Andre L.L. Aquino, Paulo R.S. Silva Filho, Elizabeth F. Wanner, and Ricardo A. RabeloCompressive Sensing and Its Application in Wireless Sensor Networks; Jae-Gun Choi, Sang-Jun Park, and Heung-No LeeCompressive Sensing for Wireless Sensor Networks; Mohammadreza Mahmudimanesh, Abdelmajid Khelil, and Neeraj SuriA Framework for Detecting Attacks on Sensors of Water Systems; Kebina Manandhar, Xiaojun Cao, and Fei HuINTELLIGENT SENSOR NETWORKS: SENSORS AND SENSOR NETWORKSReliable and Energy-Efficient Networking Protocol Design in Wireless Sensor Networks; Ting Zhu and Ping YiAgent-Driven Wireless Sensors Cooperation for Limited Resources Allocation; Sameh Abdel-Naby, Conor Muldoon, Olga Zlydareva, and Gregory O’HareEvent Detection in Wireless Sensor Networks; Norman Dziengel, Georg Wittenburg, Stephan Adler, Zakaria Kasmi, Marco Ziegert, and Jochen SchillerDynamic Coverage Problems in Sensor Networks; Hristo Djidjev and Miodrag PotkonjakSelf-Organizing Distributed State Estimator; Joris Sijs and Zoltan PappLow-Power Solutions for Wireless Passive Sensor Network Node Processor Architecture; Vyasa Sai, Ajay Ogirala, and Marlin H. MickleFusion of Pre/Post-RFID Correction Techniques to Reduce Anomalies; Peter Darcy, Prapassara Pupunwiwat, and Bela StanticRadio Frequency Identification Systems and Sensor Integration for Telemedicine; Ajay Ogirala, Shruti Mantravadi, and Marlin H. MickleIntroduction: A New Generation of Intrusion Detection Networks; Jerry Krill, Michael O’Driscoll, and Natalie N. Dickins

Dr. Fei Hu is currently an associate professor in the Department of Electrical and Computer Engineering at the University of Alabama (main campus), Tuscaloosa, Alabama. He received his PhDs from Tongji University (Shanghai, China) in the field of signal processing (in 1999) and from Clarkson University (New York) in the field of electrical and computer engineering (in 2002). He has published over 150 journal/conference papers and book chapters. Dr. Hu’s research has been supported by U.S. NSF, Cisco, Sprint, and other sources. His research expertise can be summarized as 3S—security, signals, and sensors: (1) security, which includes cyberphysical system security and medical security issues; (2) signals, which refers to intelligent signal processing, that is, using machine learning algorithms to process sensing signals; and (3) sensors, which includes wireless sensor network design issues.Dr. Qi Hao is currently an assistant professor in the Department of Electrical and Computer Engineering at The University of Alabama, Tuscaloosa, Alabama. He received his PhD from Duke University, Durham, North Carolina, in 2006, and his BE and ME from Shanghai Jiao Tong University, China, in 1994 and 1997, respectively, all in electrical engineering. His postdoctoral training in the Center for Visualization and Virtual Environment at The University of Kentucky was focused on 3Dcomputer vision for human tracking and identification. His current research interests include smart sensors, intelligent wireless sensor networks, and distributed information processing. His research has been supported by U.S. NSF and other sources.