WMN-PSODGA - An Intelligent Hybrid Simulation System for WMNs Considering Load Balancing: A Comparison for Different Client Distributions

Seiji Ohara, Ermioni Qafzezi, Admir Barolli, Shinji Sakamoto, Yi Liu and Leonard Barolli
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Seiji Ohara: Fukuoka Institute of Technology, Fukuoka, Japan
Ermioni Qafzezi: Fukuoka Institute of Technology, Fukuoka, Japan
Admir Barolli: Aleksander Moisiu University of Durres, Durres, Albania
Shinji Sakamoto: Seikei University, Musashino, Japan
Yi Liu: Fukuoka Institute of Technology, Fukuoka, Japan
Leonard Barolli: Fukuoka Institute of Technology, Fukuoka, Japan

International Journal of Distributed Systems and Technologies (IJDST), 2020, vol. 11, issue 4, 39-52

Abstract: Wireless mesh networks (WMNs) are becoming an important networking infrastructure because they have many advantages, such as low cost and increased high-speed wireless Internet connectivity. In the authors' previous work, they implemented a hybrid simulation system based on particle swarm optimization (PSO) and distributed genetic algorithm (DGA), called WMN-PSODGA. Moreover, they added to the fitness function a new parameter for mesh router load balancing a number of covered mesh clients per router (NCMCpR). In this article, the authors consider Exponential, Weibull, and Normal distributions of mesh clients and carry out a comparison study. The simulation results show that the performance of the Exponential, Weibull and Normal distributions was improved by considering load balancing when using WMN-PSODGA. For the same number of mesh clients, the Normal distribution behaves better than the other distributions. This is because all mesh clients are covered by a smaller number of mesh routers and the standard deviation is improved by effectively using NCMCpR.

Date: 2020
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