›› 2013, Vol. 28 ›› Issue (3): 553-563.doi: 10.1007/s11390-013-1355-z

Special Issue: Computer Networks and Distributed Computing

• Computer Network • Previous Articles     Next Articles

A Monte Carlo Enhanced PSO Algorithm for Optimal QoM in Multi-Channel Wireless Networks

Hua-Zheng Du1 (杜华争), Student Member, IEEE, Na Xia1,* (夏娜), Senior Member, CCF, ACM, IEEE, Jian-Guo Jiang1 (蒋建国), Senior Member, CCF, Li-Na Xu1 (徐丽娜), Student Member, IEEE, and Rong Zheng2 (郑榕), Senior Member, ACM, IEEE   

  1. 1. School of Computer and Information, Hefei University of Technology, Hefei 230009, China;
    2. Department of Computing and Software, McMaster University, Hamilton L8S4K1, Canada
  • Received:2012-07-10 Revised:2012-07-10 Online:2013-05-05 Published:2013-05-05
  • Contact: 10.1007/s11390-013-1355-z
  • Supported by:

    This work was supported by the National Natural Science Foundation of China under Grant Nos. 61100211 and 61003307, the Central High School Basic Research Foundation of China under Grant No. 2011HGZL0010, and the Postdoctoral Science Foundation of China under Grant Nos. 20110490084 and 2012T50569.

In wireless monitoring networks, wireless sniffers are distributed in a region to monitor the activities of users. It can be used for fault diagnosis, resource management and critical path analysis. Due to hardware limitations, wireless sniffers typically can only collect information on one channel at a time. Therefore, it is a key topic to optimize the channel selection for sniffers to maximize the information collected, so as to maximize the quality of monitoring (QoM) of the network. In this paper, a particle swarm optimization (PSO)-based solution is proposed to achieve the optimal channel selection. A 2D mapping particle coding and its moving scheme are devised. Monte Carlo method is incorporated to revise the solution and significantly improve the convergence of the algorithm. The extensive simulations demonstrate that the Monte Carlo enhanced PSO (MC-PSO) algorithm outperforms the related algorithms evidently with higher monitoring quality, lower computation complexity, and faster convergence. The practical experiment also shows the feasibility of this algorithm.

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