Networked Filtering and Fusion in Wireless Sensor Networks: 1st Edition (Paperback) book cover

Networked Filtering and Fusion in Wireless Sensor Networks

1st Edition

By Magdi S. Mahmoud, Yuanqing Xia

CRC Press

576 pages | 178 B/W Illus.

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Description

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.

Reviews

"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

Table of Contents

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

About the Authors

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.

Subject Categories

BISAC Subject Codes/Headings:
COM043000
COMPUTERS / Networking / General
TEC061000
TECHNOLOGY & ENGINEERING / Mobile & Wireless Communications