Sparsity-Aware Sensor Collaboration for Linear Coherent Estimation

TL;DR

This paper develops optimization methods for sparse sensor collaboration in distributed linear estimation, balancing energy and information constraints, and jointly selecting sensors to improve estimation efficiency.

Contribution

It introduces tractable optimization formulations for sparse sensor collaboration under energy and information constraints, including joint sensor selection and collaboration schemes.

Findings

Near-optimal collaboration solutions achieved in numerical experiments.

Trade-offs identified between sensor selection and collaboration.

Unified framework for sensor selection and collaboration design.

Abstract

In the context of distributed estimation, we consider the problem of sensor collaboration, which refers to the act of sharing measurements with neighboring sensors prior to transmission to a fusion center. While incorporating the cost of sensor collaboration, we aim to find optimal sparse collaboration schemes subject to a certain information or energy constraint. Two types of sensor collaboration problems are studied: minimum energy with an information constraint; and maximum information with an energy constraint. To solve the resulting sensor collaboration problems, we present tractable optimization formulations and propose efficient methods which render near-optimal solutions in numerical experiments. We also explore the situation in which there is a cost associated with the involvement of each sensor in the estimation scheme. In such situations, the participating sensors must be chosen judiciously. We introduce a unified framework to jointly design the optimal sensor selection and collaboration schemes. For a given estimation performance, we show empirically that there exists a trade-off between sensor selection and sensor collaboration.