An efficient mesh router nodes placement in wireless mesh networks based on moth‐flame optimization algorithm. (13th March 2023)

• Record Type: Journal Article
• Authors: Nouri, Nabil abdelkader; Aliouat, Zibouda; Naouri, Abdenacer; Hassak, Soufiene ali
• Is Part Of: International journal of communication systems. Volume 36:Number 8(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-03-13
• Subjects: clients coverage -- mesh routers placement -- Moth‐flame optimization algorithms -- network connectivity -- wireless mesh networks; Telecommunication systems -- Periodicals; 621.382
• DOI: 10.1002/dac.5468
• Languages: English
• ISSNs: 1074-5351

Abstract

Summary: One of the most challenging tasks is deploying a wireless mesh network backbone to achieve optimum client coverage. Previous research proposed a bi‐objective 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. Abstract : In this work, to avoid fragmented network scenarios, 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. 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.