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Mathematical Problems in Engineering
Volume 2014, Article ID 721718, 13 pages
http://dx.doi.org/10.1155/2014/721718
Research Article

Green Clustering Implementation Based on DPS-MOPSO

Yang Lu,1 Xuezhi Tan,1,2 Yun Mo,1 and Lin Ma1,2

1Communication Research Center, Harbin Institute of Technology, Harbin 150080, China
2Key Laboratory of Police Wireless Digital Communication, Ministry of Public Security, Harbin 150080, China

Received 12 May 2014; Revised 11 August 2014; Accepted 7 September 2014; Published 21 October 2014

Academic Editor: Gisele Mophou

Copyright © 2014 Yang Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A green clustering implementation is proposed to be as the first method in the framework of an energy-efficient strategy for centralized enterprise high-density WLANs. Traditionally, to maintain the network coverage, all of the APs within the WLAN have to be powered on. Nevertheless, the new algorithm can power off a large proportion of APs while the coverage is maintained as the always-on counterpart. The proposed algorithm is composed of two parallel and concurrent procedures, which are the faster procedure based on -means and the more accurate procedure based on Dynamic Population Size Multiple Objective Particle Swarm Optimization (DPS-MOPSO). To implement green clustering efficiently and accurately, dynamic population size and mutational operators are introduced as complements for the classical MOPSO. In addition to the function of AP selection, the new green clustering algorithm has another new function as the reference and guidance for AP deployment. This paper also presents simulations in scenarios modeled with ray-tracing method and FDTD technique, and the results show that about 67% up to 90% of energy consumption can be saved while the original network coverage is maintained during periods when few users are online or when the traffic load is low.