An Electromagnetism-like mechanism algorithm for the router node placement in wireless mesh networks

In this paper, we consider the problem of mesh router placement in a wireless mesh network (WMN). The latter is an emerging networking technology consisting of three kinds of nodes: mesh clients, mesh routers and gateways. Mesh routers form a backbone to forward data between client nodes and the ext...

Full description

Saved in:
Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2019-06, Vol.23 (12), p.4407-4419
Main Authors: Sayad, Lamri, Bouallouche-Medjkoune, Louiza, Aissani, Djamil
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computational Intelligence
Computer networks
Connectivity
Control
Electromagnetism
Engineering
Finite element method
Genetic algorithms
Heuristic methods
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Network topologies
Nodes
Optimization
Placement
Robotics
Routers
Swarm intelligence
Wireless networks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, we consider the problem of mesh router placement in a wireless mesh network (WMN). The latter is an emerging networking technology consisting of three kinds of nodes: mesh clients, mesh routers and gateways. Mesh routers form a backbone to forward data between client nodes and the external network. Therefore, the optimization of mesh routers positions strongly influences the performance of the WMN. Since this issue has already been proved as being computationally NP-hard to solve, the use of non-exact methods (such as heuristics and metaheuristics) is indispensable. In this sense, our current work consists to apply and adapt the electromagnetism-like mechanism (EM) metaheuristic to solve the router node placement issue. The idea is to consider a population of solutions encoded as particles subject to attractions and repulsions as in electromagnetic systems. Finally, we have evaluated our proposed approach by simulating different scenarios under various settings. The obtained results indicate that the proposed EM algorithm outperforms the existing particle swarm intelligence algorithm and genetic algorithm in defining near optimal positions for mesh routers with regard to coverage and connectivity.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-018-3096-y