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One of the most challenging tasks is deploying a wireless mesh network backbone to achieve optimum client coverage. Previous research proposed a biobjective function and used a hierarchical or aggregate weighted sum method to find the best mesh router placement. In this work, to avoid the fragmented network scenarios generated by previous formulations, we suggest and evaluate a new objective function to maximize client coverage while simultaneously optimizing and maximizing network connectivity for optimal efficiency without requiring knowledge of the aggregation coefficient. In addition, we compare the performance of several recent meta-heuristic algorithms: Moth-Flame Optimization (MFO), Marine Predators Algorithm (MPA), Multi-Verse Optimizer (MVO), Improved Grey Wolf Optimizer (IGWO), Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Harris Hawks Optimization (HHO), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), and Slime Mould Algorithm (SMA). We empirically examined the performance of the proposed function using different settings.The results show that our proposed function provides higher client coverage and optimal network connectivity with less computation power. Also, compared to other optimization algorithms, the MFO algorithm gives higher coverage to clients while maintaining a fully connected network.clients coverage, mesh routers placement, Moth-flame optimization algorithms, network connectivity, wireless mesh networks Recently, the pervasive use of WiFi devices has increased dramatically. The penetration rates of this emerging technology are massive in many deployment areas, thanks to the decreasing prices of wireless devices. A wireless mesh network (WMN) could be formed by hundreds of hotspots covering multiple regions or a single hotspot covering a small area. As illustrated in Figure 1, a generic wireless mesh network consists of mesh routers (MRs) and mesh clients (MCs). In this architecture, mesh routers are static nodes, forming the wireless backbone. Mesh clients are mobile users who can access the network through these routers.

Various ranging techniques are frequently employed in wireless sensor networks (WSNs) to determine the distance between a node and its neighboring anchor nodes. The distance measurement, as mentioned earlier is subsequently employed to estimate the location of the node whose location is unknown. The present paper presents an Accurate Localization Scheme that utilizes Grey Wolf Optimization (GWO) and is based on the Radio Signal Strength (RSS) ranging technique. The efficiency of our technique has been proved through extensive simulations, showing a consistent improvement in localization accuracy ratios and a decrease in location errors while maintaining cost-effectiveness.