Distributed estimation with ad hoc wireless sensor networks

We consider distributed estimation of a deterministic parameter vector using an ad hoc wireless sensor network. The estimators derived are obtained as solutions of constrained convex optimization problems. Using the method of multipliers in conjunction with a block coordinate descent approach we demonstrate how the resultant algorithms can be decomposed into a set of simpler tasks suitable for distributed implementation. We show that these algorithms have guaranteed convergence to properly defined optimum estimators, and further exemplify their applicability to solving estimation problems where the signal model is completely or partially known at individual sensors. Through numerical experiments we illustrate that our algorithms outperform existing alternatives.

1. Introduction

A major challenge in wireless sensor networks (WSNs) is the computation of parameter estimates based on distributed observations collected at individual sensors. Severe energy and bandwidth limitations call for the design and implementation of distributed algorithms that are efficient in terms of reducing communication overhead and computational cost.