Optimal Sensing and Data Estimation in a Large Sensor Network

TL;DR

This paper introduces iterative, randomized algorithms for energy-efficient sensor subset selection in large networks, optimizing estimation accuracy while minimizing activated sensors, with proven optimality and fast convergence.

Contribution

It develops novel Gibbs sampling-based and stochastic approximation algorithms for sensor selection, including methods for parametric distribution learning and time-varying data scenarios.

Findings

Algorithms converge rapidly to optimal solutions

Proposed methods improve energy efficiency in sensor networks

Effective for practical large-scale sensor deployment

Abstract

An energy efficient use of large scale sensor networks necessitates activating a subset of possible sensors for estimation at a fusion center. The problem is inherently combinatorial; to this end, a set of iterative, randomized algorithms are developed for sensor subset selection by exploiting the underlying statistics. Gibbs sampling-based methods are designed to optimize the estimation error and the mean number of activated sensors. The optimality of the proposed strategy is proven, along with guarantees on their convergence speeds. Also, another new algorithm exploiting stochastic approximation in conjunction with Gibbs sampling is derived for a constrained version of the sensor selection problem. The methodology is extended to the scenario where the fusion center has access to only a parametric form of the joint statistics, but not the true underlying distribution. Therein, expectation-maximization is effectively employed to learn the distribution. Strategies for iid time-varying data are also outlined. Numerical results show that the proposed methods converge very fast to the respective optimal solutions, and therefore can be employed for optimal sensor subset selection in practical sensor networks.