1st Edition

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

Edited By Fei Hu, Qi Hao Copyright 2013
    674 Pages 330 B/W Illustrations
    by CRC Press

    674 Pages 330 B/W Illustrations
    by CRC Press

    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 Processingconsiders 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
    • Networkingfocuses 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.

    Machine Learning Basics; Krasimira Kapitanova and Sang H. Son
    Modeling Unreliable Data and Sensors: Using Event Log Performance and F-Measure Attribute Selection; Vasanth Iyer, S. Sitharama Iyengar, and Srinivas Srivathsan
    Intelligent Sensor Interfaces and Data Format; Konstantin Mikhaylov, Joni Jamsa, Mika Luimula, Jouni Tervonen, and Ville Autio
    Smart Wireless Sensor Nodes for Structural Health Monitoring; Xuefeng Liu, Shaojie Tang, and Xiaohua Xu
    Knowledge 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 Rowaihy
    Prediction-Based Data Collection in Wireless Sensor Networks; Jann-Aël Le Borgne and Gianluca Bontempi
    Neuro-Disorder Patient Monitoring via Gait Sensor Networks: Toward an Intelligent, Context-Oriented Signal Processing; Fei Hu, Qingquan Sun, and Qi Hao
    Cognitive Wireless Sensor Networks; Sumit Kumar, Deepti Singhal, and Rama Murthy Garimella

    Routing for Signal Processing; Wanzhi Qiu and Efstratios Skafidas
    On-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 Pagano
    Signal Processing for Sensing and Monitoring Civil Infrastructure Systems; Mustafa Gul and F. Necati Catbas
    Data Cleaning in Low Powered Wireless Sensor Networks; Qutub Ali Bakhtiar, Niki Pissinou, and Kia Makki
    Sensor Stream Reduction; Andre L.L. Aquino, Paulo R.S. Silva Filho, Elizabeth F. Wanner, and Ricardo A. Rabelo
    Compressive Sensing and Its Application in Wireless Sensor Networks; Jae-Gun Choi, Sang-Jun Park, and Heung-No Lee
    Compressive Sensing for Wireless Sensor Networks; Mohammadreza Mahmudimanesh, Abdelmajid Khelil, and Neeraj Suri
    A Framework for Detecting Attacks on Sensors of Water Systems; Kebina Manandhar, Xiaojun Cao, and Fei Hu

    Reliable and Energy-Efficient Networking Protocol Design in Wireless Sensor Networks; Ting Zhu and Ping Yi
    Agent-Driven Wireless Sensors Cooperation for Limited Resources Allocation; Sameh Abdel-Naby, Conor Muldoon, Olga Zlydareva, and Gregory O’Hare
    Event Detection in Wireless Sensor Networks; Norman Dziengel, Georg Wittenburg, Stephan Adler, Zakaria Kasmi, Marco Ziegert, and Jochen Schiller
    Dynamic Coverage Problems in Sensor Networks; Hristo Djidjev and Miodrag Potkonjak
    Self-Organizing Distributed State Estimator; Joris Sijs and Zoltan Papp
    Low-Power Solutions for Wireless Passive Sensor Network Node Processor Architecture; Vyasa Sai, Ajay Ogirala, and Marlin H. Mickle
    Fusion of Pre/Post-RFID Correction Techniques to Reduce Anomalies; Peter Darcy, Prapassara Pupunwiwat, and Bela Stantic
    Radio Frequency Identification Systems and Sensor Integration for Telemedicine; Ajay Ogirala, Shruti Mantravadi, and Marlin H. Mickle
    Introduction: 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.