Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts.

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.

BASICS. Significance of Intelligent Sensor Networks. Elements of Intelligent Sensor Networks. Recent Advances and Applications. SENSING AND SAMPLING. Sensors for Multi-format Signals. Sampling Principle and Architecture. Bio-inspired Sensing. Compressive Sensing (CS) Principle. CS Signal Recovery. Hardware and Software Design for Compressive Sensing. DISTRIBUTED SIGNAL PROCESSING. Sensing Signal Features. Sensing Signal Processing. Networked Processing. Distributed Estimation. Distributed Prediction. INTELLIGENT SIGNAL LEARNING. Machine Learning Basics. Supervise Sensor Signal Learning. Unsupervised Sensor Signal Learning. Variational Bayes for Sensor Signal Learning. Information Geometry for Intelligent Sensor Networking.