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Volume 28 Issue 3
May  2013
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Hua-Zheng Du, Na Xia, Jian-Guo Jiang, Li-Na Xu, Rong Zheng. A Monte Carlo Enhanced PSO Algorithm for Optimal QoM in Multi-Channel Wireless Networks[J]. Journal of Computer Science and Technology, 2013, 28(3): 553-563. DOI: 10.1007/s11390-013-1355-z
Citation: Hua-Zheng Du, Na Xia, Jian-Guo Jiang, Li-Na Xu, Rong Zheng. A Monte Carlo Enhanced PSO Algorithm for Optimal QoM in Multi-Channel Wireless Networks[J]. Journal of Computer Science and Technology, 2013, 28(3): 553-563. DOI: 10.1007/s11390-013-1355-z
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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, 
  • Rong Zheng2 (郑榕) Senior Member, ACM, IEEE

  • 1. School of Computer and Information, Hefei University of Technology, Hefei 230009, China;
    2. Department of Computing and Software, McMaster University, Hamilton L8S4K1, Canada

Funds: 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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  • Received Date: July 09, 2012
  • Revised Date: July 09, 2012
  • Published Date: May 04, 2013
    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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    • References (31)
  • [1]
    Zander J, Kim S L, Almgren M et al. Radio Resource Management for Wireless Networks. Norwood, Massachusetts: Artech House Inc., 2001.
    [2]
    Correia L M, Zeller D, Blume O et al. Challenges and enabling technologies for energy aware mobile radio networks. IEEE Communications Magazine, 2010, 48(11): 66-72.
    [3]
    Liu Y H, Liu K B, Li M. Passive diagnosis for wireless sensor networks. IEEE/ACM Transactions on Networking, 2010, 18(4): 1132-1144.
    [4]
    Jin J, Zhao B, Zhou H. DLDCA: A distributed link-weighted and distance-constrained channel assignment for single-radio multi-channel wireless mesh networks. In Proc. the 2009 International Conference on Wireless Communications and Signal Processing, Nov. 2009, pp.1-5.
    [5]
    Campbell C, Loo K K, Comley R. A new MAC solution for multi-channel single radio in wireless sensor networks. In Proc. the 7th International Symposium on Wireless Communication Systems, Sept. 2010, pp.907-911.
    [6]
    Chhetri A, Nguyen H, Scalosub G, Zheng R. On quality of monitoring for multi-channel wireless infrastructure networks. In Proc. the 11th ACM International Symposium on Mobile Ad hoc Networking and Computing, Sept. 2010, pp.111-120.
    [7]
    Wu S L, Lin C Y, Tseng Y C, Sheu J P. A new multi-channel MAC protocol with on-demand channel assignment for multihop mobile ad hoc networks. In Proc. the 2000 International Symposium on Parallel Architectures, Algorithms and Networks (I-SPAN), Dec. 2000, pp.232-237.
    [8]
    Bahl P, Chandra R, Dunagan J. SSCH: Slotted seeded channel hopping for capacity improvement in IEEE 802.11 adhoc wireless networks. In Proc. the 10th Annual International Conference on Mobile Computing and Networking, Sept. 2004, pp.216-230.
    [9]
    So J, Vaidya N H. Multi-channel MAC for ad hoc networks: Handling multi-channel hidden terminals using a single transceiver. In Proc. the 5th ACM International Symposium on Mobile Ad hoc Networking and Computing, May 2004, pp.222-233.
    [10]
    Hou F, Huang J. Dynamic channel selection in cognitive radio network with channel heterogeneity. In Proc. the 2010 IEEE Global Communications Conference, Dec. 2010, pp.1-6.
    [11]
    Du Z G, Hong P L, ZhouWY et al. ICCA: Interface-clustered channel assignment in multi-radio wireless mesh networks. Acta Electronic Sinica, 2011, 39(3): 723-726. (In Chinese)
    [12]
    Chaudhry A U, Ahmad N, Hafez R H M. Improving throughput and fairness by improved channel assignment using topology control based on power control for multi-radio multichannel wireless mesh networks. EURASIP Journal on Wireless Communications and Networking, 2012, 155: 1-25.
    [13]
    Zhou Y Q, Wang J Z, Sawahashi M. Downlink transmission of broadband OFCDM systems——Part I: Hybrid detection. IEEE Transactions on Communications, 2005, 53(4): 718729.
    [14]
    Zhu H L, Wang J Z. Chunk-based resource allocation in OFDMA systems——Part I: Chunk allocation. IEEE Transactions on Communications, 2009, 57(9): 2734-2744.
    [15]
    Yeo J, Youssef M, Agrawala A. A framework for wireless LAN monitoring and its applications. In Proc. the 3rd ACM Workshop on Wireless Security, Oct. 2004, pp.70-79.
    [16]
    Yeo J, Youssef M, Henderson T, Agrawala A. An accurate technique for measuring the wireless side of wireless networks. In Proc. the 2005 Workshop on Wireless Traffic Measurements and Modeling, Jun. 2005, pp.13-18.
    [17]
    Rodrig M, Reis C, Mahajan R, Wetherall D, Zahorjan J. Measurement-based characterization of 802.11 in a hotspot setting. In Proc. the 2005 ACM SIGCOMM Workshop on Experimental Approaches to Wireless Network Design and Analysis, Aug. 2005, pp.5-10.
    [18]
    Cheng Y C, Bellardo J, Benk·o P et al. Jigsaw: Solving the puzzle of enterprise 802.11 analysis. In Proc. the 2006 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, Sept. 2006, pp.3950.
    [19]
    Yang W G, Guo T D, Zhao T. An optimal lifetime model and its solution of a heterogeneous surveillance sensor network. Chinese Journal of Computers, 2007, 30(4): 532-538. (in Chinese)
    [20]
    Liu C, Cao G. Distributed monitoring and aggregation in wireless sensor networks. In Proc. the 29th Conference on Information Communications, Mar. 2010, pp.1-9.
    [21]
    Shin D H, Bagchi S. Optimal monitoring in multi-channel multi-radio wireless mesh networks. In Proc. the 10th ACM International Symposium on Mobile Ad Hoc Networking and Computing, May 2009, pp.229-238.
    [22]
    Leung K K, Kim B J. Frequency assignment for IEEE 802.11 wireless networks. In Proc. the 58th Vehicular Technology Conference, Oct. 2003, Vol.13, pp.1422-1426.
    [23]
    Chekuri C, Kumar A. Maximum coverage problem with group budget constraints and applications. In Proc. the 7th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, Aug. 2004, pp.72-83.
    [24]
    Arora P, Xia N, Zheng R. A Gibbs sampler approach for optimal distributed monitoring of multi-channel wireless networks. In Proc. the 2011 IEEE Global Communications Conference, Dec. 2011, pp.1-6.
    [25]
    Kennedy J, Eberhart R C. Particle swarm optimization. In Proc. the IEEE Conference on Neural Networks, Nov. 1995, Vol.4, pp.1942-1948.
    [26]
    Eberhart R C, Kennedy J. A new optimizer using particles swarm theory. In Proc. the 6th International Symposium on Micro Machine and Human Science, Oct. 1995, pp.39-43.
    [27]
    Kennedy J, Eberhart R C. A discrete binary version of the particle swarm algorithm. In Proc. the IEEE Conference on Systems, Man, and Cybernetics, Oct. 1997, Vol.5, pp.41044109.
    [28]
    Liao C, Tseng C, Luarn P. A discrete version of particle swarm optimization for flowshop scheduling problems. Computers and Operations Research, 2007, 34(10): 3099-3111.
    [29]
    Xia N, Han D, Zhang G F et al. Study on attitude determination based on discrete particle swarm optimization. Science China Technological Sciences, 2010, 53(12): 3397-3403.
    [30]
    Bremaud P. Markov Chains: Gibbs Field, Monte Carlo Simulation, and Queues. New York: Springer, 1999.
    [31]
    Kau?mann B, Baccelli F, Chaintreau A et al. Measurementbased self organization of interfering 802.11 wireless access networks. In Proc. the 26th IEEE International Conference on Computer Communications, May 2007. pp.1451-1459.
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