Distributed Estimation over Wireless Sensor Networks with Packet Losses

TL;DR

This paper presents a decentralized adaptive algorithm for estimating a time-varying signal in wireless sensor networks, effectively handling noisy measurements and packet losses without central coordination.

Contribution

It introduces a novel distributed estimation method with adaptive weights, providing convergence conditions and an efficient strategy for optimal weight computation.

Findings

Algorithm performs well under various network topologies.

Robust to different packet loss probabilities.

Theoretical analysis confirms stability and minimized variance.

Abstract

A distributed adaptive algorithm to estimate a time-varying signal, measured by a wireless sensor network, is designed and analyzed. One of the major features of the algorithm is that no central coordination among the nodes needs to be assumed. The measurements taken by the nodes of the network are affected by noise, and the communication among the nodes is subject to packet losses. Nodes exchange local estimates and measurements with neighboring nodes. Each node of the network locally computes adaptive weights that minimize the estimation error variance. Decentralized conditions on the weights, needed for the convergence of the estimation error throughout the overall network, are presented. A Lipschitz optimization problem is posed to guarantee stability and the minimization of the variance. An efficient strategy to distribute the computation of the optimal solution is investigated. A theoretical performance analysis of the distributed algorithm is carried out both in the presence of perfect and lossy links. Numerical simulations illustrate performance for various network topologies and packet loss probabilities.