Welcome to the InfoSci Platform
Reference Hub7
A Simulated Annealing Based Centre of Mass (SAC) Approach for Mesh Routers Placement in Rural Areas

A Simulated Annealing Based Centre of Mass (SAC) Approach for Mesh Routers Placement in Rural Areas

Jean Louis Kedieng Ebongue Fendji, Chris Thron
Copyright: © 2020 |Volume: 11 |Issue: 1 |Pages: 29
ISSN: 1947-9328|EISSN: 1947-9336|EISBN13: 9781799806530|DOI: 10.4018/IJORIS.2020010102
Cite Article Cite Article

MLA

Fendji, Jean Louis Kedieng Ebongue, and Chris Thron. "A Simulated Annealing Based Centre of Mass (SAC) Approach for Mesh Routers Placement in Rural Areas." IJORIS vol.11, no.1 2020: pp.37-65. http://doi.org/10.4018/IJORIS.2020010102

APA

Fendji, J. L. & Thron, C. (2020). A Simulated Annealing Based Centre of Mass (SAC) Approach for Mesh Routers Placement in Rural Areas. International Journal of Operations Research and Information Systems (IJORIS), 11(1), 37-65. http://doi.org/10.4018/IJORIS.2020010102

Chicago

Fendji, Jean Louis Kedieng Ebongue, and Chris Thron. "A Simulated Annealing Based Centre of Mass (SAC) Approach for Mesh Routers Placement in Rural Areas," International Journal of Operations Research and Information Systems (IJORIS) 11, no.1: 37-65. http://doi.org/10.4018/IJORIS.2020010102

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

The problem of node placement in a rural wireless mesh network (RWMN) consists of determining router placement which minimizes the number of routers while providing good coverage of the area of interest. This problem is NP-hard with a factorial complexity. This article introduces a new approach, called the simulated annealing-based centre of mass (SAC) for solving this placement problem. The intent of this approach is to improve the robustness and the quality of solution, and to minimize the convergence time of a simulated annealing (SA) approach in solving the same problem in small and large scale. SAC is compared to the centre of mass (CM) and simulated annealing (SA) approaches. The performances of these algorithms were evaluated on a set of 24 instances. The experimental results show that the SAC approach provides the best robustness and solution quality, while decreasing by half the convergence time of the SA algorithm.