MEGA: Maximum-Entropy Genetic Algorithm for Router Nodes Placement in Wireless Mesh Networks
Ussipov N. Akhtanov S. Turlykozhayeva D. Temesheva S. Akhmetali A. Zaidyn M. Namazbayev T. Bolysbay A. Akniyazova A. Tang X.
October 2024Multidisciplinary Digital Publishing Institute (MDPI)
Sensors
2024#24Issue 20
Over the past decade, wireless mesh networks (WMNs) have seen significant advancements due to their simple deployment, cost-effectiveness, ease of implementation, and reliable service coverage. However, despite these advantages, the placement of nodes in WMNs presents a critical challenge that significantly impacts their performance. This issue is recognized as an NP-hard problem, underscoring the necessity of development optimization algorithms, such as heuristic and metaheuristic approaches. This motivated us to develop the Maximum Entropy Genetic Algorithm (MEGA) to address the issue of mesh router node placement in WMNs. To assess the proposed method, we conducted experiments across various scenarios with different settings, focusing on key metrics such as network connectivity and user coverage. The simulation results showed the comparative performance of MEGA in relation to other prominent algorithms, such as the Coyote Optimization Algorithm (COA), Firefly Algorithm (FA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), revealing MEGA’s effectiveness and usability in determining optimal locations for mesh routers.
entropy , genetic algorithm , mesh router nodes placement , network connectivity , user coverage , wireless mesh networks
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Faculty of Physics and Technology, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
School of Information and Communication Engineering, Xi’an Jiaotong University, Xi’an, 710049, China
Faculty of Physics and Technology
School of Information and Communication Engineering
10 лет помогаем публиковать статьи Международный издатель
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