Intelligent Sensor Networks The Integration of Sensor Networks, Signal Processing and Machine Learning OTHER TELECOMMUNICATIONS BOOKS FROM AUERBACH Ad Hoc Mobile Wireless Networks: Principles, Protocols, and Applications Subir Kumar Sarkar, T.G. Basavaraju, and C. Puttamadappa ISBN 978-1-4200-6221-2 Communication and Networking in Smart Grids Yang Xiao (Editor) ISBN 978-1-4398-7873-6 Delay Tolerant Networks: Protocols and Applications Athanasios V. Vasilakos, Yan Zhang, and Thrasyvoulos Spyropoulos ISBN 978-1-4398-1108-5 Emerging Wireless Networks: Concepts, Techniques and Applications Christian Makaya and Samuel Pierre (Editors) ISBN 978-1-4398-2135-0 Game Theory in Communication Networks: Cooperative Resolution of Interactive Networking Scenarios Josephina Antoniou and Andreas Pitsillides ISBN 978-1-4398-4808-1 Green Communications: Theoretical Fundamentals, Algorithms and Applications Jinsong Wu, Sundeep Rangan, and Honggang Zhang ISBN 978-1-4665-0107-2 Green Communications and Networking F. Richard Yu, Xi Zhang, and Victor C.M. Leung (Editors) ISBN 978-1-4398-9913-7 Green Mobile Devices and Networks: Energy Optimization and Scavenging Techniques Hrishikesh Venkataraman and Gabriel-Miro Muntean (Editors) ISBN 978-1-4398-5989-6 Handbook on Mobile Ad Hoc and Pervasive Communications Laurence T. Yang, Xingang Liu, and Mieso K. Denko (Editors) ISBN 978-1-4398-4616-2 IP Telephony Interconnection Reference: Challenges, Models, and Engineering Mohamed Boucadair, Isabel Borges, Pedro Miguel Neves, and Olafur Pall Einarsson ISBN 978-1-4398-5178-4 LTE-Advanced Air Interface Technology Xincheng Zhang and Xiaojin Zhou ISBN 978-1-4665-0152-2 Media Networks: Architectures, Applications, and Standards Hassnaa Moustafa and Sherali Zeadally (Editors) ISBN 978-1-4398-7728-9 Multihomed Communication with SCTP (Stream Control Transmission Protocol) Victor C.M. Leung, Eduardo Parente Ribeiro, Alan Wagner, and Janardhan Iyengar ISBN 978-1-4665-6698-9 Multimedia Communications and Networking Mario Marques da Silva ISBN 978-1-4398-7484-4 Near Field Communications Handbook Syed A. Ahson and Mohammad Ilyas (Editors) ISBN 978-1-4200-8814-4 Next-Generation Batteries and Fuel Cells for Commercial, Military, and Space Applications A. R. Jha, ISBN 978-1-4398-5066-4 Physical Principles of Wireless Communications, Second Edition Victor L. Granatstein, ISBN 978-1-4398-7897-2 Security of Mobile Communications Noureddine Boudriga, ISBN 978-0-8493-7941-3 Smart Grid Security: An End-to-End View of Security in the New Electrical Grid Gilbert N. Sorebo and Michael C. Echols ISBN 978-1-4398-5587-4 Transmission Techniques for 4G Systems Mário Marques da Silva ISBN 978-1-4665-1233-7 Transmission Techniques for Emergent Multicast and Broadcast Systems Mário Marques da Silva, Americo Correia, Rui Dinis, Nuno Souto, and Joao Carlos Silva ISBN 978-1-4398-1593-9 TV Content Analysis: Techniques and Applications Yiannis Kompatsiaris, Bernard Merialdo, and Shiguo Lian (Editors) ISBN 978-1-4398-5560-7 TV White Space Spectrum Technologies: Regulations, Standards, and Applications Rashid Abdelhaleem Saeed and Stephen J. Shellhammer ISBN 978-1-4398-4879-1 Wireless Sensor Networks: Current Status and Future Trends Shafiullah Khan, Al-Sakib Khan Pathan, and Nabil Ali Alrajeh ISBN 978-1-4665-0606-0 Wireless Sensor Networks: Principles and Practice Fei Hu and Xiaojun Cao ISBN 978-1-4200-9215-8 AUERBACH PUBLICATIONS www.auerbach-publications.com 5P0SEFS$BMMr'BY E-mail: orders@crcpress.com Boca Raton London New York CRC Press is an imprint of the Taylor & Francis Group, an informa business Intelligent Sensor Networks The Integration of Sensor Networks, Signal Processing and Machine Learning Edited by FEI HU QI HAO MATLAB® is a trademark of The MathWorks, Inc. and is used with permission. The MathWorks does not warrant the accuracy of the text or exercises in this book. This book’s use or discussion of MATLAB® software or related products does not consti- tute endorsement or sponsorship by The MathWorks of a particular pedagogical approach or particular use of the MATLAB® software. CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2013 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Printed in the United States of America on acid-free paper Version Date: 20121023 International Standard Book Number: 978-1-1381-9974-3 (Paperback) International Standard Book Number: 978-1-4398-9281-7 (Hardback) This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material repro-duced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. The Open Access version of this book, available at www.taylorfrancis.com, has been made availableunder a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identifica- tion and explanation without intent to infringe. Library of Congress Cataloging-in-Publication Data Intelligent sensor networks : the integration of sensor networks, signal processing, and machine learning / editors, Fei Hu, Qi Hao. p. cm. Summary: “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”-- Provided by publisher. Includes bibliographical references and index. ISBN 978-1-4398-9281-7 (hardback) 1. Wireless sensor networks. I. Hu, Fei, 1972- II. Hao, Qi, 1973- TK7872.D48I4685 2012 681’.2--dc23 2012032571 Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com To Yang Fang and Gloria (Ge Ge) Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi Editors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii PART I Intelligent Sensor Networks: Machine Learning Approach 1 Machine Learning Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . KRASIMIRA KAPITANOVA AND SANG H. SON 3 2 Modeling Unreliable Data and Sensors: Using Event Log Performance and F-Measure Attribute Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VASANTH IYER, S. SITHARAMA IYENGAR, AND NIKI PISSINOU 31 3 Intelligent Sensor Interfaces and Data Format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 KONSTANTIN MIKHAYLOV, JONI JAMSA, MIKA LUIMULA, JOUNI TERVONEN, AND VILLE AUTIO 4 Smart Wireless Sensor Nodes for Structural Health Monitoring . . . . . . . . . . . . . . . . . . . . . . . 77 XUEFENG LIU, SHAOJIE TANG, AND XIAOHUA XU 5 Knowledge Representation and Reasoning for the Design of Resilient Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 DAVID KELLER, TOURIA EL-MEZYANI, SANJEEV SRIVASTAVA, AND DAVID CARTES 6 Intelligent Sensor-to-Mission Assignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 HOSAM ROWAIHY 7 Prediction-Based Data Collection in Wireless Sensor Networks. . . . . . . . . . . . . . . . . . . . . . . . 153 YANN-AËL LE BORGNE AND GIANLUCA BONTEMPI 8 Neuro-Disorder Patient Monitoring via Gait Sensor Networks: Toward an Intelligent, Context-Oriented Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 FEI HU, QINGQUAN SUN, AND QI HAO 9 Cognitive Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 SUMIT KUMAR, DEEPTI SINGHAL, AND RAMA MURTHY GARIMELLA vii viii ■ Contents PART II Intelligent Sensor Networks: Signal Processing 10 Routing for Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 WANZHI QIU AND EFSTRATIOS SKAFIDAS 11 On-Board Image Processing in Wireless Multimedia Sensor Networks: A Parking Space Monitoring Solution for Intelligent Transportation Systems . . . . . . . . . . . . . . . . . . . . 245 CLAUDIO SALVADORI, MATTEO PETRACCA, MARCO GHIBAUDI, AND PAOLO PAGANO 12 Signal Processing for Sensing and Monitoring of Civil Infrastructure Systems . . . . . . . . 267 MUSTAFA GUL AND F. NECATI CATBAS 13 Data Cleaning in Low-Powered Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 QUTUB ALI BAKHTIAR, NIKI PISSINOU, AND KIA MAKKI 14 Sensor Stream Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 ANDRE L.L. AQUINO, PAULO R.S. SILVA FILHO, ELIZABETH F. WANNER, AND RICARDO A. RABELO 15 Compressive Sensing and Its Application in Wireless Sensor Networks . . . . . . . . . . . . . . . . 351 JAE-GUN CHOI, SANG-JUN PARK, AND HEUNG-NO LEE 16 Compressive Sensing for Wireless Sensor Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 MOHAMMADREZA MAHMUDIMANESH, ABDELMAJID KHELIL, AND NEERAJ SURI 17 Framework for Detecting Attacks on Sensors of Water Systems . . . . . . . . . . . . . . . . . . . . . . . . 397 KEBINA MANANDHAR, XIAOJUN CAO, AND FEI HU PART III Intelligent Sensor Networks: Sensors and Sensor Networks 18 Reliable and Energy-Efficient Networking Protocol Design in Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411 TING ZHU AND PING YI 19 Agent-Driven Wireless Sensors Cooperation for Limited Resources Allocation . . . . . . . 427 SAMEH ABDEL-NABY, CONOR MULDOON, OLGA ZLYDAREVA, AND GREGORY O’HARE 20 Event Detection in Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441 NORMAN DZIENGEL, GEORG WITTENBURG, STEPHAN ADLER, ZAKARIA KASMI, MARCO ZIEGERT, AND JOCHEN SCHILLER 21 Dynamic Coverage Problems in Sensor Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459 HRISTO DJIDJEV AND MIODRAG POTKONJAK 22 Self-Organizing Distributed State Estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483 JORIS SIJS AND ZOLTAN PAPP Contents ■ ix 23 Low-Power Solutions for Wireless Passive Sensor Network Node Processor Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515 VYASA SAI, AJAY OGIRALA, AND MARLIN H. MICKLE 24 Fusion of Pre/Post-RFID Correction Techniques to Reduce Anomalies . . . . . . . . . . . . . . . 529 PETER DARCY, PRAPASSARA PUPUNWIWAT, AND BELA STANTIC 25 Radio Frequency Identification Systems and Sensor Integration for Telemedicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 567 AJAY OGIRALA, SHRUTI MANTRAVADI, AND MARLIN H. MICKLE 26 A New Generation of Intrusion Detection Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589 JERRY KRILL AND MICHAEL O’DRISCOLL Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 635 Preface Nowadays, various sensors are used to collect the data of our environment, including RFID, video cameras, pressure, acoustic, etc. A typical sensor converts the physical energy of the object under examination into electrical signals to deduce the information of interest. Intelligent sensing technology utilizes proper prior knowledge and machine learning techniques to enhance the process of information acquisition. Therefore, the whole sensing procedure can be performed at three levels: data, information, and knowledge. At the data level, each sample represents a measure of target energy within a certain temporal-spatial volume. For example, the pixel value of a video camera represents the number of photons emitted within a certain area during a specific time window. The information acquired by a sensor is represented by a probabilistic belief over random variables. For example, the information of target position can be represented by a Gaussian distribution. The knowledge acquired by a sensor is represented by a statistical model describing relations among random variables. For example, the behavior of a target can be represented by a hierarchical hidden Markov model; a situation can be represented by a hierarchical Bayesian network. The unprecedented advances in wireless networking technology enable the deployment of a large number of sensors over a wide area without limits of wires. However, the main challenge for developing wireless sensor networks is limited resources: power supply, computing complexity, and communication bandwidth. There are several possible solutions to overcome these obstacles: (1) development of “smart” sensing nodes that can reduce the data volume for information representa tion as well as energy consumption; (2) development of distributed computing “intelligence” that allows data fusion, state estimation, and machine learning to be performed in a distributed way; and (3) development of ad hoc networking “intelligence” that can guarantee the connectivity of sensor systems under various conditions. Besides, energy harvest technologies and powerless sensor networks have been developed to relax the limits of power supply. The “intelligence” of sensor networks can be achieved in four ways: (1) spatial awareness, (2) data awareness, (3) group awareness, and (4) context awareness. Spatial awareness refers to an intelligent sensor network’s capability of knowing the relative geometric information of its members and targets under examination. This awareness is implemented through sensor self-calibration and target state estimation. Data awareness refers to an intelligent sensor network’s capability of reducing the data volume for information representation. This awareness is implemented through data prediction, data fusion, and statistical model building. Group awareness refers to an intelligent sensor network’s capability of all members knowing each other’s states and adjusting each member’s behavior according to other members’ actions. For example, distributed estimation and data learning are performed through the collaboration of a group of sensor nodes; ad hoc networking techniques maintain the connectivity of the whole network. Context awareness refers to an intelligent sensor xi xii ■ Preface network’s capability of changing its operation modes based on the knowledge of situations and resources to achieve maximum efficiency of sensing performance. This awareness is implemented through proper context representation and distributed inference. Book Highlights So far there are no published books on intelligent sensor network from a machine learning and signal processing perspective. Unlike current sensor network books, this book has contributions from world-famous sensing experts that deal with the following issues: 1. Emphasize “intelligent” designs in sensor networks : The intelligence of sensor networks can be developed through distributed machine learning or smart sensor design. Machine learn ing of sensor networks can be performed in supervised, unsupervised, or semisupervised ways. In Part I, Chapter 1—Machine Learning Basics—we have provided a comprehen sive picture of this area. Machine learning technology is a multidisciplinary field that includes probability and statistics, psychology, information theory, and artificial intelli gence. Note that sensor networks often operate in very challenging conditions and need to accommodate environmental changes, hardware degradation, and inaccurate sensor read ings. Thus they should learn and adapt to the changes in their operation environment. Machine learning can be used to achieve intelligent learning and adaptation. In Part I we have also included some chapters that emphasize these “intelligence” aspects of sensor net works. For example, we have explained the components of the intelligent sensor (transducer) interfacing problem. We have also discussed how the network can intelligently choose the “best” assignment from the available sensors to the missions to maximize the utility of the network. 2. Detail signal processing principles in intelligent sensor networks : Recently, a few advanced signal processing principles have been applied in sensor networks. For example, compressive sensing is an efficient and effective signal acquisition and sampling framework for sensor networks. It can save transmittal and computational power significantly at the sensor node. Its signal acquisition and compression scheme is very simple , so it is suitable for inexpensive sensors. As another example, a Kalman filter can be used to identify the sensor data pollution attacks in sensor networks. 3. Elaborate important platforms on intelligent sensor networks : The platforms of intelligent sensor networks include smart sensors, RFID-assisted nodes, and distributed self-organization architecture. This book covers these platforms. For example, in Part III we have included two chapters on RFID-based sensor function enhancement. The sensor/RFID integration can make the sensor better identify and trace surrounding objects. 4. Explain interesting applications on intelligent sensor networks : Intelligent sensor networks can be used for target tracking, object identification, structural health monitoring, and other important applications. In most chapters, we have clearly explained how those “intelligent” designs can be used for realistic applications. For example, in structural health monitoring applications, we can embed the sensors in a concrete bridge. Thus, a bridge fracture can be detected in time. Those embedded sensors for field applications can be powered through solar-cell batteries. In healthcare applications, we can use medical sensors and intelligent body area sensor networks to achieve low-cost, 24/7 patient monitoring from a remote office. Preface ■ xiii Targeted Audience This book is suitable for the following types of readers: 1. College students : This book can serve as a textbook or reference book for college courses on intelligent sensor networks. The courses could be offered in computer science, electrical and computer engineering, information technology and science, or other departments. 2. Researchers : Because each chapter is written by leading experts, the contents will be very useful for researchers (such as graduate students and professors) who are interested in the application of artificial intelligence and signal processing in sensor networks. 3. Computer scientists : We have provided many computing algorithms on machine learning and data processing in this book. Thus, computer scientists could refer to those principles in their own design. 4. Engineers : We have also provided useful intelligent sensor node/sensor network design examples. Thus, company engineers could use those principles in their product design. Book Architecture This book includes three parts as follows: Part I—Machine Learning: This part describes the application of machine learning and other artificial intelligence principles in sensor network intelligence. It covers the basics of machine learning, including smart sensor/transducer architecture and data representation for intelligent sensors, modal parameter–based structural health monitoring based on wireless smart sensors, sensor-mission assignment problems in which the objective is to maximize the overall utility of the network under different constraints, reducing the amount of communication in sensor networks by means of learning techniques, neurodisorder patient monitoring via gait sensor networks, and cognitive radio-based sensor networks. Part II—Signal Processing: This part describes the optimization of sensor network performance based on digital signal processing (DSP) techniques. It includes the following important topics: cross-layer integration of routing and application-specific signal processing, on-board image pro cessing in wireless multimedia sensor networks for intelligent transportation systems, and essential signal processing and data analysis methods to effectively handle and process the data acquired with the sensor networks for civil infrastructure systems. 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, a basic methodology that is composed of four phases (characterization, reduction tools, robustness, and conception), discussions on how the compressive sensing (CS) can be used as a useful framework for the sensor networks to compress and acquire signals and save transmittal and computational power in sensors, and the use of Kalman filters for attack detection in a water system sensor network that consists of water level sensors and velocity sensors. Part III—Networking: This part focuses on detailed network protocol design in order to achieve an intelligent sensor networking scenario. It covers the following topics: energy-efficient oppor tunistic routing protocol for sensor networking; multi-agent-driven wireless sensor cooperation for limited resource allocation; an illustration of how distributed event detection can achieve both high accuracy and energy efficiency; blanket/sweep/barrier coverage issues in sensor networks; lin ear state-estimator, locally and additionally, to perform management procedures that support the network of state-estimators to establish self-organization; low-power solution for wireless passive xiv ■ Preface sensor network; the fusion of pre/post RFID correction techniques to reduce anomalies; RFID systems and sensor integration for tele-medicine; and the new generation of intrusion detection sensor networks. Disclaimer: We have tried our best to provide credits to all cited publications in this book. We sincerely thank all authors who have published materials on intelligent sensor network and who have directly/indirectly contributed to this book through our citations. If you have questions on the contents of this book, please contact the editors Fei Hu (fei@eng.ua.edu) or Qi Hao (qh@eng.ua.edu). We will correct any errors and thus improve this book in future editions. MATLAB � is a registered trademark of The Mathworks, Inc. For product information, please contact: The MathWorks, Inc. 3 Apple Hill Drive Natick, MA, 01760-2098 USA Tel: 508-647-7000 Fax: 508-647-7001 E-mail: info@mathworks.com Web: www.mathworks.com Editors Dr. Fei Hu is currently an associate professor in the Department of Electrical and Computer Engineering at The University of Alabama, 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. National Science Foundation, 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 Depart ment 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 3D computer vision for human tracking and identification. His current research inter ests include smart sensors, intelligent wireless sensor networks, and distributed information processing. His research has been supported by U.S. National Science Foundation and other sources. xv Contributors Sameh Abdel-Naby Center for Sensor Web Technologies University College Dublin Dublin, Ireland Stephan Adler Department of Mathematics and Computer Science Computer Systems and Telematics Freie Universität Berlin Berlin, Germany Andre L.L. Aquino Computer Institute Federal University of Alagoas Alagoas, Brazil Ville Autio RF Media Laboratory Centria University of Applied Sciences Ylivieska, Finland Qutub Ali Bakhtiar Network Laboratory Technological University of America Coconut Creek, Florida Gianluca Bontempi Machine Learning Group Department of Computer Science Université Libre de Bruxelles Brussels, Belgium Xiaojun Cao Department of Computer Science Georgia State University Atlanta, Georgia David Cartes Center for Advanced Power Systems Florida State University Tallahassee, Florida F. Necati Catbas Department of Civil, Environmental and Construction Engineering University of Central Florida Orlando, Florida Jae-Gun Choi Department of Information and Communications Gwangju Institute of Science and Technology Gwangju, South Korea Peter Darcy Information and Communication Technology School of Information and Communication Technology Institute for Integrated and Intelligent Systems Griffith University Brisbane, Queensland, Australia Hristo Djidjev Information Sciences Los Alamos National Laboratory Los Alamos, New Mexico xvii xviii ■ Contributors Norman Dziengel Department of Mathematics and Computer Science Computer Systems and Telematics Freie Universität Berlin Berlin, Germany Touria El-Mezyani Center for Advanced Power Systems Florida State University Tallahassee, Florida Paulo R.S. Silva Filho Computer Institute Federal University of Alagoas Alagoas, Brazil Rama Murthy Garimella International Institute of Information Technology Hyderabad, India Marco Ghibaudi Real-Time Systems Laboratory Scuola Superiore Sant’Anna and Scuola Superiore Sant’Anna Research Unit National Inter-University Consortium for Telecommunications Pisa, Italy Mustafa Gul Department of Civil and Environmental Engineering University of Alberta Edmonton, Alberta, Canada Qi Hao Department of Electrical and Computer Engineering The University of Alabama Tuscaloosa, Alabama Fei Hu Department of Electrical and Computer Engineering The University of Alabama Tuscaloosa, Alabama S. Sitharama Iyengar School of Computer and Information Sciences Florida International University Miami, Florida Vasanth Iyer School of Computer and Information Sciences Florida International University Miami, Florida Joni Jamsa RF Media Laboratory Centria University of Applied Sciences Ylivieska, Finland Krasimira Kapitanova Department of Computer Science University of Virginia Charlottesville, Virginia Zakaria Kasmi Department of Mathematics and Computer Science Computer Systems and Telematics Freie Universität Berlin Berlin, Germany David Keller Center for Advanced Power Systems Florida State University Tallahassee, Florida Abdelmajid Khelil Department of Computer Science Technical University of Darmstadt Darmstadt, Germany Jerry Krill Applied Physics Laboratory Johns Hopkins University Laurel, Maryland Contributors ■ xix Sumit Kumar International Institute of Information Technology Hyderabad, India Yann-Aël Le Borgne Machine Learning Group Department of Computer Science Université Libre de Bruxelles Brussels, Belgium Heung-No Lee Department of Information and Communications Gwangju Institute of Science and Technology Gwangju, South Korea Xuefeng Liu Department of Computing Hong Kong Polytechnic University Kowloon, Hong Kong, People’s Republic of China Mika Luimula Faculty of Telecommunication and e-Business Turku University of Applied Sciences Turku, Finland Mohammadreza Mahmudimanesh Department of Computer Science Technical University of Darmstadt Darmstadt, Germany Kia Makki Technological University of America Coconut Creek, Florida Kebina Manandhar Department of Computer Science Georgia State University Atlanta, Georgia Shruti Mantravadi Army College of Dental Sciences Dr. NTR University of Health Sciences Secunderabad, India Marlin H. Mickle Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh, Pennsylvania Konstantin Mikhaylov Oulu Southern Institute University of Oulu Ylivieska, Finland Conor Muldoon Center for Sensor Web Technologies University College Dublin Dublin, Ireland Michael O’Driscoll (retired) Applied Physics Laboratory Department of Research and Exploratory Development Johns Hopkins University Laurel, Maryland Ajay Ogirala Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh, Pennsylvania Gregory O’Hare Center for Sensor Web Technologies University College Dublin Dublin, Ireland Paolo Pagano National Laboratory of Photonic Networks National Interuniversity Consortium for Telecommunications and Real-Time Systems Laboratory Scuola Superiore Sant’Anna Pisa, Italy Zoltan Papp Instituut voor Toegepast Natuurkundig Onderzoek Oude Waalsdorperweg Den Haag, the Netherlands