Networked Filtering and Fusion in Wireless Sensor Networks

By exploiting the synergies among available data, information fusion can reduce data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. Networked Filtering and Fusion in Wireless Sensor Networks introduces the subject of multi-sensor fusion as the method of choice for implementing distributed systems.The book examines the state of the art in information fusion. It presents the known methods, algorithms, architectures, and models of information fusion and discusses their applicability in the context of wireless sensor networks (WSNs). Paying particular attention to the wide range of topics that have been covered in recent literature, the text presents the results of a number of typical case studies.Complete with research supported elements and comprehensive references, this teaching-oriented volume uses standard scientific terminology, conventions, and notations throughout. It applies recently developed convex optimization theory and highly efficient algorithms in estimation fusion to open up discussion and provide researchers with an ideal starting point for further research on distributed estimation and fusion for WSNs.The book supplies a cohesive overview of the key results of theory and applications of information-fusion-related problems in networked systems in a unified framework. Providing advanced mathematical treatment of fundamental problems with information fusion, it will help you broaden your understanding of prospective applications and how to address such problems in practice.After reading the book, you will gain the understanding required to model parts of dynamic systems and use those models to develop distributed fusion control algorithms that are based on feedback control theory.

IntroductionOverview Fundamental TermsSome LimitationsInformation Fusion in Wireless Sensor NetworkClassifying Information Fusion Classification based on relationship among the sources Classification based on levels of abstraction Classification based on input and outputOutline of the Book Methodology Chapter organization Notes Proposed Topics Wireless Sensor NetworksSome DefinitionsCommon Characteristics Required Mechanisms Related Ingredients Key issues Types of sensor networks Main advantages Sensor Networks Applications Military applications Environmental applications Health applications Application trends Hardware constraintsRouting Protocols System architecture and design issues Flooding and gossiping Sensor protocols for information via negotiation Directed diffusion Geographic and energy-aware routing Gradient-based routing Constrained anisotropic diffusion routing Active query forwarding Low-energy adaptive clustering hierarchy Power-efficient gathering Adaptive threshold sensitive energy efficient network Minimum energy communication network Geographic adaptive fidelity Sensor Selection Schemes Sensor selection problem Coverage schemes Target tracking and localization schemes Single mission assignment schemes Multiple mission assignment schemes Quality of Service Management QoS requirements Challenges Wireless Sensor Network Security Obstacles of sensor security Security requirements NotesProposed Topics Distributed Sensor Fusion Assessment of Distributed State Estimation Introduction Consensus-based distributed Kalman filter Simulation example 1Distributed Sensor Fusion Introduction Consensus problems in networked systems Consensus filters Simulation example 2 Simulation example 3 Some observations Estimation for Adaptive Sensor Selection Introduction Distributed estimation in dynamic systems Convergence properties Sensor selection for target tracking Selection of best active set Global node selection Spatial split Computational complexity Number of active sensors Simulation resultsMulti-Sensor Management Primary purpose Role in information fusion Architecture classes Hybrid and hierarchical architectures Classification of related problems NotesProposed TopicsDistributed Kalman FilteringIntroductionDistributed Kalman Filtering Methods Different methods Pattern of applications Diffusion-based filtering Multi-sensor data fusion systems Distributed particle filtering Self-tuning based filteringInformation Flow Micro-Kalman filters Frequency-type consensus filters Simulation example 1 Simulation example 2Consensus Algorithms in Sensor Networked Systems Basics of graph theory Consensus algorithms Simulation example 3 Simulation example 4Application of Kalman Filter Estimation Preliminaries 802.11 distributed coordination function Estimating the Competing Stations ARMA filter estimation Extended Kalman filter estimation Discrete state model Extended Kalman filter Selection of state noise statistics Change detection Performance evaluation Notes Proposed TopicsExpectation Maximization General Considerations Data-Fusion Fault Diagnostics Scheme Modeling with sensor and actuator faults Actuator faults Sensor faults The Expected maximization algorithm Initial system estimation Computing the input moments Fault Isolation System description Fault model for rotational hydraulic drive Fault scenarios EM Algorithm Implementation Leakage fault Controller faultNotesProposed Topics Wireless Estimation MethodsPartitioned Kalman Filters Introduction Centralized Kalman filter Parallel information filter Decentralized information filter Hierarchical Kalman filter Distributed Kalman filter with weighted averaging Distributed consensus Kalman filter Distributed Kalman filter with bipartite fusion graphs Simulation example A Wireless Networked Control System Sources of wireless communication errorsStructure of the WNCS Networked control design Simulation example B NotesProposed Topics Multi-Sensor Fault Estimation Introduction Model-based schemes Model-free schemes Probabilistic schemesProblem StatementImproved Multi-Sensor Data Fusion Technique Unscented Kalman filter Unscented transformation Multi-sensor integration architectures Centralized integration method Decentralized integration method Simulation Results An interconnected-tank process model Utility boilerNotesProposed TopicsMulti-Sensor Data Fusion Overview Multi-sensor data fusion Challenging problems Multi–sensor data fusion approaches Multi–sensor algorithms Fault Monitoring Introduction Problem Formulation Discrete time UKF Unscented procedure Parameter estimation Improved MSDF techniques NotesProposed TopicsApproximate Distributed EstimationIntroductionProblem Formulation Fusion with Complete Prior Information Modified Kalman filter-I Lower-bound KF-I Upper-bound KF-I Convergence Fusion without Prior Information Modified Kalman filter-II Upper-bound KF-IIFusion with Incomplete Prior Information Modified Kalman filter-III Approximating the Kalman filter Lower-bound KF-III Upper-bound KF-III Fusion Algorithm Evaluation and Testing Simulation results Time computation Notes Proposed Topics Estimation via Information MatrixIntroductionProblem Formulation Covariance Intersection Covariance Intersection Filter Algorithm Complete feedback case Partial feedback case Weighted Covariance Algorithm Complete feedback case Partial feedback caseKalman-Like Particle Filter Algorithm Complete feedback case Partial feedback caseMeasurement Fusion AlgorithmEquivalence of Two Measurement Fusion Methods Tracking Level Cases Illustrative example 1 Illustrative example 2Testing and Evaluation Fault model for utility boiler Covariance intersection filter Weighted covariance filter Kalman-like particle filter Mean square error comparison NotesProposed TopicsFiltering in Sensor NetworksDistributed H8 Filtering Introduction System analysis Simulation example 1 Distributed Cooperative Filtering Introduction Problem formulationCentralized estimation Distributed estimation Issues of implementation Distributed Consensus Filtering Introduction Problem formulation Filter design: fully-equipped controllers Filter design: pinning controllers Simulation example 2 Distributed Fusion Filtering Introduction Problem statement Two-stage distributed estimation Distributed fusion algorithm Simulation example 3 Distributed Filtering over Finite Horizon Introduction Problem description Performance analysis Distributed H8 consensus filters design Simulation example 4 NotesProposed Topics Appendix A Glossary of Terminology and Notations General Terms Functional Differential Equations Stability Notions Practical stabilizability Razumikhin stability Delay Patterns Lyapunov Stability Theorems Lyapunov-Razumikhin theorem Lyapunov-Krasovskii theorem Some Lyapunov-Krasovskii functionals Algebraic Graph Theory Basic results Laplacian spectrum of graphs Properties of adjacency matrix Minimum Mean Square Estimate Gronwall–Bellman Inequalities Basic Inequalities Inequality 1 Inequality 2 Inequality 3 Inequality 4 (Schur Complements) Inequality 5 Inequality 6 Bounding lemmas Linear Matrix Inequalities Basics Some Standard Problems S-Procedure Some Formulas on Matrix Inverses Inverse of Block Matrices Matrix inversion lemma Irreducible matrices

Magdi Sadek Mahmoud obtained BSc (Honors) in communication engineering, MSc in electronic engineering, and PhD in systems engineering, all from Cairo University in 1968, 1972, and 1974, respectively. He has been a professor of engineering since 1984. He is now a Distinguished University Professor at King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia. He was on the faculty at different universities worldwide including Egypt (CU, AUC), Kuwait (KU), UAE (UAEU), UK (UMIST), USA (Pitt, Case Western), Singapore (Nanyang Technological) and Australia (Adelaide). He lectured in Venezuela (Caracas), Germany (Hanover), UK (Kent), USA (University of Texas at SA), Canada (Montreal, Alberta) and China (BIT, Yanshan). He is the principal author of thirty-four (34) books, inclusive book-chapters and the author/co-author of more than 510 peer-reviewed papers. He is the recipient of two national, one regional, and four university prizes for outstanding research in engineering and applied mathematics. He is a fellow of the IEE, a senior member of the IEEE, the CEI (UK), and a registered consultant engineer of information engineering and systems (Egypt). He is currently actively engaged in teaching and research in the development of modern methodologies of distributed control and filtering, networked-control systems, triggering mechanisms in dynamical systems, faulttolerant systems and information technology. He is a fellow of the IEE, a senior member of the IEEE, the CEI (UK), and a registered consultant engineer of information engineering and systems Egypt.Yuanqing Xia was born in Anhui Province, China, in 1971 and graduated from the Department of Mathematics, Chuzhou University, Chuzhou, China, in 1991. He received his MS degree in Fundamental Mathematics from Anhui University, China, in 1998 and his PhD degree in Control Theory and Control Engineering from Beijing University of Aeronautics and Astronautics, Beijing, China, in 2001. From 1991-1995, he was with Tongcheng Middle-School as a teacher, Anhui, China. During January 2002 to November 2003, he was a postdoctoral research associate at the Institute of Systems Science, Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing, China, where he worked on navigation, guidance and control. From November 2003 to February 2004, he joined the National University of Singapore as a research fellow, where he worked on variable structure control. From February 2004 to February 2006, he was with the University of Glamorgan, Pontypridd, U.K., as a research fellow, where he worked on networked control systems. From February 2007 to June 2008, he was a guest professor with Innsbruck Medical University, Innsbruck, Austria, where he worked on biomedical signal processing. Since July 2004, he has been with the Department of Automatic Control, Beijing Institute of Technology, Beijing, first as an associate professor, and then, since 2008, as a professor. His current research interests are in the fields of networked control systems, robust control, sliding mode control, active disturbance rejection control and biomedical signal processing.