Access Point Design with a Genetic Algorithm

The interest in deploying local wireless networks has increased in the corporate environment, in recent years, as a result of several improvements in their features. Nevertheless, there are some problems caused by inadequate positions of access points (APs) which overload some cells of the total area to be covered. Some strategies of AP positioning aim only at covering the environment. Some aspects, such as, the number of users per AP and reducing the distance from the users to an AP, could be objective function parameters in the network optimization problem. This article presents a novel model to AP design, where the area covered and the users connected are maximized, and the number of APs is minimized. Two different algorithms to deal with the AP design are presented, the greedy search heuristic and a genetic algorithm. Three experimental studies with different areas to be covered were conducted. in all of them, both algorithms reached their targets, i.e., all the grid area was covered and all users were served.

[1]  Antonio Alfredo Ferreira Loureiro,et al.  Optimal Network Design for Wireless Local Area Network , 2001, Ann. Oper. Res..

[2]  Thom W. Frühwirth,et al.  Placing Base Stations in Wireless Indoor Communication Networks , 2000, IEEE Intell. Syst..

[3]  Ajay Gupta,et al.  Performance Indicators in a 802.11 WLAN Deployment , 2009, 2009 International Conference on Advances in Recent Technologies in Communication and Computing.

[4]  Margaret H. Wright,et al.  Optimization methods for base station placement in wireless applications , 1998, VTC '98. 48th IEEE Vehicular Technology Conference. Pathway to Global Wireless Revolution (Cat. No.98CH36151).

[5]  Yongxiang Zhao,et al.  Indoor Access Points Location Optimization Using Differential Evolution , 2008, 2008 International Conference on Computer Science and Software Engineering.

[6]  Jens Vygen,et al.  The Book Review Column1 , 2020, SIGACT News.

[7]  N. Pierce Origin of Species , 1914, Nature.

[8]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .