Dynamic Topology Adaptation Based on Adaptive Link Selection Algorithms for Distributed Estimation

TL;DR

This paper introduces adaptive link selection algorithms for distributed estimation in wireless sensor networks and smart grids, enhancing accuracy, convergence speed, and robustness by intelligently selecting network links.

Contribution

It proposes novel adaptive link selection algorithms based on LMS/RLS that improve estimation accuracy and robustness in networks with poor-quality links.

Findings

Algorithms achieve more accurate estimates.

Faster convergence compared to existing methods.

Enhanced robustness against link failures.

Abstract

This paper presents adaptive link selection algorithms for distributed estimation and considers their application to wireless sensor networks and smart grids. In particular, exhaustive search--based least--mean--squares(LMS)/recursive least squares(RLS) link selection algorithms and sparsity--inspired LMS/RLS link selection algorithms that can exploit the topology of networks with poor--quality links are considered. The proposed link selection algorithms are then analyzed in terms of their stability, steady--state and tracking performance, and computational complexity. In comparison with existing centralized or distributed estimation strategies, key features of the proposed algorithms are: 1) more accurate estimates and faster convergence speed can be obtained; and 2) the network is equipped with the ability of link selection that can circumvent link failures and improve the estimation performance. The performance of the proposed algorithms for distributed estimation is illustrated via simulations in applications of wireless sensor networks and smart grids.