Optimized Sensor Collaboration for Estimation of Temporally Correlated Parameters

TL;DR

This paper develops an optimized sensor collaboration strategy for estimating temporally correlated parameters, transforming the problem into a nonconvex optimization and proposing efficient algorithms for near-optimal solutions.

Contribution

It introduces a novel approach using convex-concave procedures and ADMM to optimize sensor collaboration for correlated parameters, improving computational efficiency and estimation accuracy.

Findings

Effective collaboration schemes improve estimation accuracy.

Parameter correlation and temporal dynamics significantly impact performance.

Proposed algorithms scale well with network size.

Abstract

In this paper, we aim to design the optimal sensor collaboration strategy for the estimation of time-varying parameters, where collaboration refers to the act of sharing measurements with neighboring sensors prior to transmission to a fusion center. We begin by addressing the sensor collaboration problem for the estimation of uncorrelated parameters. We show that the resulting collaboration problem can be transformed into a special nonconvex optimization problem, where a difference of convex functions carries all the nonconvexity. This specific problem structure enables the use of a convex-concave procedure to obtain a near-optimal solution. When the parameters of interest are temporally correlated, a penalized version of the convex-concave procedure becomes well suited for designing the optimal collaboration scheme. In order to improve computational efficiency, we further propose a fast algorithm that scales gracefully with problem size via the alternating direction method of multipliers. Numerical results are provided to demonstrate the effectiveness of our approach and the impact of parameter correlation and temporal dynamics of sensor networks on estimation performance.