Strategies for Distributed Sensor Selection Using Convex Optimization

TL;DR

This paper explores distributed sensor selection for parameter estimation, proposing convex optimization heuristics that significantly improve performance over naive decentralized methods, approaching centralized optimal results.

Contribution

It introduces two convex optimization-based heuristics for distributed sensor selection that outperform naive methods and approximate centralized performance.

Findings

Heuristics outperform naive decentralized selection

Performance approaches that of centralized selection

Convex optimization effectively guides sensor selection

Abstract

Consider the estimation of an unknown parameter vector in a linear measurement model. Centralized sensor selection consists in selecting a set of k_s sensor measurements, from a total number of m potential measurements. The performance of the corresponding selection is measured by the volume of an estimation error covariance matrix. In this work, we consider the problem of selecting these sensors in a distributed or decentralized fashion. In particular, we study the case of two leader nodes that perform naive decentralized selections. We demonstrate that this can degrade the performance severely. Therefore, two heuristics based on convex optimization methods are introduced, where we first allow one leader to make a selection, and then to share a modest amount of information about his selection with the remaining node. We will show that both heuristics clearly outperform the naive decentralized selection, and achieve a performance close to the centralized selection.