Solving the Mesh Router Nodes Placement in Wireless Mesh Networks Using Coyote Optimization Algorithm

Wireless Mesh Networks (WMNs) have rapid real developments during the last decade due to their simple implementation at low cost, easy network maintenance, and reliable service coverage. Despite these properties, the nodes placement of such networks imposes an important research issue for network op...

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.52744-52759
Main Authors: Mekhmoukh Taleb, Sylia, Meraihi, Yassine, Gabis, Asma Benmessaoud, Mirjalili, Seyedali, Zaguia, Atef, Ramdane-Cherif, Amar
Format: Article
Language:English
Subjects:
Computer Science
Costs
Coyote optimization algorithm
Finite element method
Genetic algorithms
Heuristic
Heuristic algorithms
Heuristic methods
Measurement
mesh router nodes placement
meta-heuristics
network design
Nodes
Optimization
Optimization algorithms
Particle swarm optimization
Placement
Routers
Topology
Wireless mesh networks
Wireless networks
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Summary:Wireless Mesh Networks (WMNs) have rapid real developments during the last decade due to their simple implementation at low cost, easy network maintenance, and reliable service coverage. Despite these properties, the nodes placement of such networks imposes an important research issue for network operators and influences strongly the WMNs performance. This challenging issue is known to be an NP-hard problem, and solving it using approximate optimization algorithms (i.e. heuristic and meta-heuristic) is essential. This motivates our attempts to present an application of the Coyote Optimization Algorithm (COA) to solve the mesh routers placement problem in WMNs in this work. Experiments are conducted on several scenarios under different settings, taking into account two important metrics such as network connectivity and user coverage. Simulation results demonstrate the effectiveness and merits of COA in finding optimal mesh routers locations when compared to other optimization algorithms such as Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), Bat Algorithm (BA), African Vulture Optimization Algorithm (AVOA), Aquila Optimizer (AO), Bald Eagle Search optimization (BES), Coronavirus herd immunity optimizer (CHIO), and Salp Swarm Algorithm (SSA).
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3166866