Fastest Distributed Consensus on Star-Mesh Hybrid Sensor Networks

TL;DR

This paper presents an analytical solution for the fastest distributed consensus problem in star-mesh hybrid sensor networks with K-partite core, using stratification and semidefinite programming, and studies convergence rates numerically.

Contribution

It introduces a novel analytical approach for FDC in star-mesh hybrid networks with symmetric properties, enhancing understanding of convergence behavior.

Findings

Analytical solution for FDC in SMHK networks

Numerical analysis of convergence rates

Impact of topological parameters on convergence

Abstract

Solving Fastest Distributed Consensus (FDC) averaging problem over sensor networks with different topologies has received some attention recently and one of the well known topologies in this issue is star-mesh hybrid topology. Here in this work we present analytical solution for the problem of FDC algorithm by means of stratification and semidefinite programming, for the Star-Mesh Hybrid network with K-partite core (SMHK) which has rich symmetric properties. Also the variations of asymptotic and per step convergence rate of SMHK network versus its topological parameters have been studied numerically.