Optimized Transmission for Consensus in Wireless Sensor Networks

TL;DR

This paper introduces an optimized, low-complexity algorithm for designing sensor gains in wireless sensor networks to improve decentralized parameter estimation accuracy using a consensus-based framework.

Contribution

It proposes a novel cyclic optimization algorithm that efficiently designs sensor gains under various constraints for enhanced estimation accuracy.

Findings

Achieves improved estimation accuracy in WSNs

Offers a low-computational-cost design method

Handles complex sensor gain constraints effectively

Abstract

In this paper, we present a consensus-based framework for decentralized estimation of deterministic parameters in wireless sensor networks (WSNs). In particular, we propose an optimization algorithm to design (possibly complex) sensor gains in order to achieve an estimate of the parameter of interest that is as accurate as possible. The proposed design algorithm employs a cyclic approach capable of handling various sensor gain constraints. In addition, each iteration of the proposed design framework is comprised of the Gram-Schmidt process and power-method like iterations, and as a result, enjoys a low-computational cost.