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

所属专题： Computer Networks and Distributed Computing

• Special Section on Selected Paper from NPC 2011 • 上一篇    下一篇

多信道无线网络中最优QoM的MC-PSO算法

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
  • 收稿日期:2012-07-10 修回日期:2012-07-10 出版日期:2013-05-05 发布日期:2013-05-05
  • 作者简介:Hua-Zheng Du received her B.E. and M.E. degrees in information and communication engineering from Hefei University of Technology, China, in 2009 and 2011, respectively. She acted as a visiting researcher in SUN CREATE Electronics CO., LTD from Jan. 2012 to Nov. 2012. She is currently working toward the Ph.D. degree in the School of Computer and Information, Hefei University of Technology. She is a student member of IEEE. Her research interests include wireless sensor networks, microwave communication, and intelligent information processing.
  • 基金资助:

    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.

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.

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在无线监测网络中,无线嗅探器被布置在网络区域内监测用户的活动。这可用于故障诊断、资源管理和关键路径分析。由于硬件的限制,无线嗅探器通常在一个时刻只能收集一个信道上的信息,因此,优化嗅探器的信道选择以最大化收集的信息,进而最大化网络监测质量(QoM)就成为一个关键问题。在本文中,作者提出一种基于粒子群优化的方法以达到优化的信道选择。设计了一种二维映射粒子编码及其移动策略；引入蒙特卡罗方法来修正进化过程中的解,显著提高了算法的收敛性。大量仿真结果表明这种蒙特卡罗增强的粒子群优化算法(MC-PSO)明显超出了相关算法的性能,并具有更高的监测质量、更低的计算复杂度、更快的收敛性。实际测试也证明了算法的有效性。

Abstract: 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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