Assessment of six router replacement methods for router placement optimisation in WMNs considering different network scales and subway mesh client distribution

In this paper, we present the implementation of a hybrid intelligent simulation system called WMN-PSOHCDGA by integrating Particle Swarm Optimisation (PSO), Hill Climbing (HC) and Distributed Genetic Algorithm (DGA). We consider Subway distribution of mesh clients and carry out simulations for six router replacement methods (CM, RIWM, LDIWM, LDVM, RDVM and FC-RDVM) and different scales of Wireless Mesh Networks (WMNs). For evaluation, we consider three metrics: network connectivity, coverage of mesh clients and load balancing. The simulation results show that all six router replacement methods have good network connectivity for both small and middle scale WMNs. For small scale WMNs, the mesh clients coverage and load balancing are almost the same. However, for middle scale WMNs, CM and FC-RDVM perform better than other router replacement methods.