Dynamic Topology Adaptation Based on Adaptive Link Selection Algorithms for Distributed Estimation
For wireless sensor networks and smart grids, this work provides a way to improve estimation accuracy and convergence speed by dynamically selecting high-quality links.
This paper proposes adaptive link selection algorithms for distributed estimation in wireless sensor networks and smart grids, achieving more accurate estimates and faster convergence compared to existing methods.
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.