Wireless Sensor Networks: Distributed Consensus Estimation

by Cailian Chen, Shanying Zhu, Xinping Guan, Xuemin (Sherman) Shen

Overview

This SpringerBrief evaluates the cooperative effort of sensor nodes to accomplish high-level tasks with sensing, data processing and communication. The metrics of network-wide convergence, unbiasedness, consistency and optimality are discussed through network topology, distributed estimation algorithms and consensus strategy. Systematic analysis reveals that proper deployment of sensor nodes and a small number of low-cost relays (without sensing function) can speed up the information fusion and thus improve the estimation capability of wireless sensor networks (WSNs). This brief also investigates the spatial distribution of sensor nodes and basic scalable estimation algorithms, the consensus-based estimation capability for a class of relay assisted sensor networks with asymmetric communication topology, and the problem of filter design for mobile target tracking over WSNs. From the system perspective, the network topology is closely related to the capability and efficiency of network-wide scalable distributed estimation. Wireless Sensor Networks: Distributed Consensus Estimation is a valuable resource for researchers and professionals working in wireless communications, networks and distributed computing. Advanced-level students studying computer science and electrical engineering will also find the content helpful.

Product Details

ISBN-13:	9783319123783
Publisher:	Springer International Publishing
Publication date:	12/10/2014
Series:	SpringerBriefs in Computer Science
Edition description:	2014
Pages:	89
Product dimensions:	6.10(w) x 9.25(h) x 0.01(d)

Table of Contents

Introduction.- Distributed Consensus Estimation of Wireless Sensor Networks.- Consensus Based Distributed Parameter Estimation with Asymmetric Communications.- Consensus Based Optimal Target Tracking in Relay Assisted Wireless Sensor Networks.- Node Deployment for Distributed Consensus Estimation.- Conclusions and Future Work.