Mesh router node placement using somersault based giant armadillo optimization

Abstract

Wireless mesh networks (WMNs) have rapidly advanced in the last decade because of their ease of experimentation at minimum cost, simple network maintenance, and consistent provision coverage. However, the placement of router nodes remains a critical research challenge for network operators, as it directly impacts the overall performance and effectiveness of the WMN. Thus, this research proposes a novel approach of the somersault fraction strategy (SFS)-based giant armadillo optimization (GAO) for addressing an issue of mesh router (MR) node placement in WMN. A significance of the proposed SFS-GAO approach was estimated in terms of different scenarios under diverse settings, considering network connectivity as well as client coverage metrics. The simulation outcomes demonstrate that the proposed SFS-GAO method achieves better results in each performance metric like coverage, connectivity, and fitness, based on a varied count of mesh clients (MC), MR, and coverage radius. The findings show that the proposed function offers higher client coverage and optimal network connectivity with minimum computation power.