Solving the Wireless Mesh Network Design Problem using Genetic Algorithm and Simulated Annealing Optimization Methods

Abstract

Mesh clients, mesh routers and gateways are components of Wireless Mesh Network (WMN). In WMN, gateways connect to Internet using wireline links and supply Internet access services for users. Multiple gateways are needed, which take time and cost budget to set up, due to the limited wireless channel bit rate. WMN is a highly developed technology that offers to end users a wireless broadband access. It offers a high degree of flexibility contrasted to conventional networks; however, this attribute comes at the expense of a more complex construction. Therefore, a challenge is the planning and optimization of WMNs. This paper concentrates on the challenge using a genetic algorithm and simulated annealing. The genetic algorithm and simulated annealing enable searching for a low-cost WMN configuration with constraints and determine the number of used gateways. Experimental results proved that the performance of the genetic algorithm and simulated annealing in minimizing WMN network costs while satisfying quality of service. The proposed models are presented to significantly outperform the existing solutions.

A Delaunay Edges and Simulated Annealing-Based Integrated Approach for Mesh Router Placement Optimization in Wireless Mesh Networks

“…In [ 22 ], they assumed a mobile environment and that the performance indicators for mesh router placement optimization are connectivity and coverage, as well as average travel distance by routers. In [ 23 ], the authors compared the performance of SA and GA for mesh router placement optimization while in [ 24 ] they compared the performance of different types of GAs (NSGA-II and the Multi-Objective Genetic Algorithm (MOGA) [ 25 ]). In [ 23 ], the objective of mesh router placement optimization was to minimize the network cost of the WMN while satisfying the QoS.…”

“…In [ 23 ], the authors compared the performance of SA and GA for mesh router placement optimization while in [ 24 ] they compared the performance of different types of GAs (NSGA-II and the Multi-Objective Genetic Algorithm (MOGA) [ 25 ]). In [ 23 ], the objective of mesh router placement optimization was to minimize the network cost of the WMN while satisfying the QoS. In [ 24 ], the authors considered as evaluation metrics for optimization coverage, reliability, and cost.…”

A comparison study of chi-square and uniform distributions of mesh clients for different router replacement methods using WMN-PSODGA hybrid intelligent simulation system

“…Machine learning and evolutionary-based algorithms are some practical approaches capable of solving computationally hard problems. These approaches, which have been applied to fields ranging from networking and telecommunications to automation and power control systems [5,7], are investigated for node placement problems in several previous works [3,9,15,18].…”

Magnify Qos with Tabu & Link Scheduling In Wmn

“…The quality of WMN are self configuring and self healing mechanisms all the mode through which the node failure or path failure is with ease improved since in WMN a node can be vigorous as client as well as a server depending on the appeal. [1][2] [4] [5] If there are any node collapse or path malfunction it habitually convalesce and convey data as swiftly as possible. In WMN each node manoeuvre as a massive amount and as lustrous router, unabashed carton on connote of preceding hop points to facilitate not in unwavering WMN transmission hotchpotch of their intention.…”