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

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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

  1. Zander J, Kim S L, Almgren M et al. Radio Resource Management for Wireless Networks. Norwood, Massachusetts: Artech House Inc., 2001.

  2. Correia LM, Zeller D, Blume O et al (2010) Challenges and enabling technologies for energy aware mobile radio networks. IEEE Communications Magazine 48(11):66–72

    Article  Google Scholar 

  3. Liu YH, Liu KB, Li M (2010) Passive diagnosis for wireless sensor networks. IEEE/ACM Transactions on Networking 18(4):1132–1144

    Article  Google Scholar 

  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 multi-hop 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 ZG, Hong PL, Zhou WY et al (2011) ICCA: Interface-clustered channel assignment in multi-radio wireless mesh networks. Acta Electronic Sinica 39(3):723–726 (In Chinese)

    Google Scholar 

  12. Chaudhry AU, Ahmad N, Hafez RHM (2012) Improving through-put and fairness by improved channel assignment using topology control based on power control for multi-radio multi-channel wireless mesh networks. EURASIP Journal on Wireless Communications and Networking 155:1–25

    Google Scholar 

  13. Zhou YQ, Wang JZ, Sawahashi M (2005) Downlink transmission of broadband OFCDM systems — Part I: Hybrid detection. IEEE Transactions on Communications 53(4):718–729

    Article  Google Scholar 

  14. Zhu HL, Wang JZ (2009) Chunk-based resource allocation in OFDMA systems — Part I: Chunk allocation. IEEE Transactions on Communications 57(9):2734–2744

    Article  Google Scholar 

  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.39–50.

  19. Yang WG, Guo TD, Zhao T (2007) An optimal lifetime model and its solution of a heterogeneous surveillance sensor network. Chinese Journal of Computers 30(4):532–538

    Google Scholar 

  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.4104–4109.

  28. Liao C, Tseng C, Luarn P (2007) A discrete version of particle swarm optimization for flowshop scheduling problems. Computers and Operations Research 34(10):3099–3111

    Article  MATH  Google Scholar 

  29. Xia N, Han D, Zhang GF et al (2010) Study on attitude determination based on discrete particle swarm optimization. Science China Technological Sciences 53(12):3397–3403

    Article  Google Scholar 

  30. Bremaud P (1999) Markov Chains: Gibbs Field, Monte Carlo Simulation, and Queues. Springer, New York

    Google Scholar 

  31. Kaufmann B, Baccelli F, Chaintreau A et al. Measurement-based 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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Authors and Affiliations

  1. School of Computer and Information, Hefei University of Technology, Hefei, 230009, China

    Hua-Zheng Du (Student Member, IEEE), Na Xia (Senior Member, CCF, ACM, IEEE), Jian-Guo Jiang (Senior Member, CCF) & Li-Na Xu (Student Member, IEEE)

  2. Department of Computing and Software, McMaster University, Hamilton, L8S4K1, Canada

    Rong Zheng (Senior Member, ACM, IEEE)

Authors
  1. Hua-Zheng Du
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  2. Na Xia
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  3. Jian-Guo Jiang
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  4. Li-Na Xu
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  5. Rong Zheng
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Corresponding author

Correspondence to Na Xia.

Additional information

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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Du, HZ., Xia, N., Jiang, JG. et al. A Monte Carlo Enhanced PSO Algorithm for Optimal QoM in Multi-Channel Wireless Networks. J. Comput. Sci. Technol. 28, 553–563 (2013). https://doi.org/10.1007/s11390-013-1355-z

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  • DOI: https://doi.org/10.1007/s11390-013-1355-z

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