1st Edition

Networked Filtering and Fusion in Wireless Sensor Networks

By Magdi S. Mahmoud, Yuanqing Xia Copyright 2015
    576 Pages 178 B/W Illustrations
    by CRC Press

    576 Pages 178 B/W Illustrations
    by CRC Press

    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.

    Introduction
    Overview
    Fundamental Terms
    Some Limitations
    Information Fusion in Wireless Sensor Network
    Classifying Information Fusion
         Classification based on relationship among the sources
         Classification based on levels of abstraction
         Classification based on input and output
    Outline of the Book
         Methodology 
         Chapter organization
    Notes
    Proposed Topics

    Wireless Sensor Networks
    Some Definitions
    Common 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 constraints
    Routing 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
    Notes
    Proposed Topics

    Distributed Sensor Fusion
    Assessment of Distributed State Estimation
         Introduction 
         Consensus-based distributed Kalman filter 
         Simulation example 1
    Distributed 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 results
    Multi-Sensor Management
         Primary purpose 
         Role in information fusion
         Architecture classes 
         Hybrid and hierarchical architectures
         Classification of related problems
    Notes
    Proposed Topics

    Distributed Kalman Filtering
    Introduction
    Distributed Kalman Filtering Methods 
         Different methods
         Pattern of applications 
         Diffusion-based filtering
         Multi-sensor data fusion systems
         Distributed particle filtering
         Self-tuning based filtering
    Information Flow
         Micro-Kalman filters
         Frequency-type consensus filters
         Simulation example 1
         Simulation example 2
    Consensus Algorithms in Sensor Networked Systems
         Basics of graph theory
         Consensus algorithms
         Simulation example 3
         Simulation example 4
    Application 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 Topics

    Expectation 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 fault
    Notes
    Proposed Topics

    Wireless Estimation Methods
    Partitioned 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 errors 
         Structure of the WNCS 
         Networked control design 
         Simulation example B
    Notes
    Proposed Topics

    Multi-Sensor Fault Estimation
    Introduction
         Model-based schemes 
         Model-free schemes
         Probabilistic schemes
    Problem Statement
    Improved 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 boiler
    Notes
    Proposed Topics

    Multi-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
    Notes
    Proposed Topics

    Approximate Distributed Estimation
    Introduction
    Problem 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-II
    Fusion 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 Matrix
    Introduction
    Problem Formulation
    Covariance Intersection
    Covariance Intersection Filter
         Algorithm
         Complete feedback case 
         Partial feedback case
    Weighted Covariance 
         Algorithm 
         Complete feedback case
         Partial feedback case
    Kalman-Like Particle Filter 
         Algorithm 
         Complete feedback case 
         Partial feedback case
    Measurement Fusion Algorithm
    Equivalence of Two Measurement Fusion Methods
    Tracking Level Cases 
         Illustrative example 1
         Illustrative example 2
    Testing and Evaluation
         Fault model for utility boiler 
         Covariance intersection filter
         Weighted covariance filter
         Kalman-like particle filter
         Mean square error comparison
    Notes
    Proposed Topics

    Filtering in Sensor Networks
    Distributed H∞ Filtering
         Introduction
         System analysis 
         Simulation example 1
    Distributed Cooperative Filtering 
         Introduction 
         Problem formulation
         Centralized 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 H∞ consensus filters design 
         Simulation example 4
    Notes
    Proposed 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

    Biography

    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.

    "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 multisensor 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. ... After reading the book, readers 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."
    IEEE Microwave Magazine, December 2015