Wireless Networks

, Volume 19, Issue 4, pp 507–516 | Cite as

Immune optimization algorithm for solving vertical handoff decision problem in heterogeneous wireless network

  • Fang Liu
  • Si-feng Zhu
  • Zheng-yi Chai
  • Yu-tao Qi
  • Jian-she Wu
Article
First Online:

Abstract

In heterogeneous wireless network environment, wireless local area network (WLAN) is usually deployed within the coverage of a cellular network to provide users with the convenience of seamless roaming among heterogeneous wireless access networks. Vertical handoffs between the WLAN and the cellular network maybe occur frequently. As for the vertical handoff performance, there is a critical requirement for developing algorithms for connection management and optimal resource allocation for seamless mobility. In this paper, we develop a mathematical model for vertical handoff decision problem, and propose a multi-objective optimization immune algorithm-based vertical handoff decision scheme. The proposed scheme can enable a wireless access network not only to balance the overall load among all base stations and access points but also maximize the collective battery lifetime of mobile terminals. Results based on a detailed performance evaluation study are also presented here to demonstrate the efficacy of the proposed scheme.

Keywords

Multi-objective optimization immune algorithm Vertical handoff decision problem Heterogeneous wireless network Load balance Battery lifetime 
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Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant 61072139, Grant 61003199, the China Postdoctoral Science Foundation funded project under Grant 20100203120008, Grant 20090461283, the National Research Foundation for the Doctoral Program of Higher Education of China under Grant 20090203120016, the Fundamental Research Funds for the Central Universities under Grant JY10000902001, Grant JY10000903007, the Provincial Natural Science Foundation of Shaanxi of China under Grant 2011JQ8010, the National Science Basic Research Plan in Henan Province of China under Grant 12A520055, Grant 112102210221.

References

  1. 1.
    Wu, G., Mizuno, M., & Havinga, P. J. (2002). MIRAI architecture for heterogeneous network. IEEE Communications Magazine, 40(2), 126–134.CrossRefGoogle Scholar
  2. 2.
    Munasinghe, K. S., & Jamalipour, A. (2010). An analytical evaluation of mobility management in integrated WLAN-UMTS networks. Computers & Electrical Engineering, 36(4), 735–751.MATHCrossRefGoogle Scholar
  3. 3.
    Abolfazl, M., Sonia, A., & Jalil, C. (2010). A location-aware vertical handoff algorithm for hybrid networks. Journal of Communications, 5(7), 521–529.Google Scholar
  4. 4.
    Liu, S. M., Pan, S., Mi, Z. K., Meng, Q. M., & Xu, M. H. (2011). A simple additive weighting vertical handoff algorithm based on SINR and AHP for heterogeneous wireless networks. Journal of Electronics and Information Technology, 33(1), 235–239.CrossRefGoogle Scholar
  5. 5.
    Kaveh, S., Alireza, A., & Leung, V. (2011). Optimal distributed vertical handoff strategies in vehicular heterogeneous networks. IEEE Journal on Selected Areas in Communications, 29(3), 534–544.CrossRefGoogle Scholar
  6. 6.
    Liu, X. W., Fang, X. M., Chen, X., & Peng, X. S. (2011). A bidding model and cooperative game-based vertical handoff decision algorithm. Journal of Network and Computer Applications, 34(4), 1263–1271.CrossRefGoogle Scholar
  7. 7.
    Chi, S., Enrique, S. N., Vahid, S. M., & Vincent, W. (2011). A constrained MDP-based vertical handoff decision algorithm for 3G heterogeneous wireless networks. Wireless Networks, 17(4), 1063–1081.CrossRefGoogle Scholar
  8. 8.
    Lee, S., Sriram, K., Kim, K., Kim, Y. H., & Golmie, N. (2009). Vertical handoff decision algorithms for providing optimized performance in heterogeneous wireless networks. IEEE Transactions on Vehicular Technology, 58(2), 865–881.CrossRefGoogle Scholar
  9. 9.
    Zitzler, E., & Thiele, L. (1999). Multi-Objective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation, 3(4), 257–271.CrossRefGoogle Scholar
  10. 10.
    Coello, C. A., & Cortés, N. C. (2005). Solving multi-objective optimization problem using an artificial immune system. Genetic Programming and Evolvable Machines, 6(2), 163–190.CrossRefGoogle Scholar
  11. 11.
    Gong, M. G., Jiao, L. C., Du, H. F., & Bo, L. F. (2008). Multi-objective immune algorithm with nondominated neighbor-based selection. Evolutionary Computation, 16(2), 225–255.CrossRefGoogle Scholar
  12. 12.
    Yang, D. D., Jiao, L. C., & Gong, M. G. (2010). Clone selection algorithm to solve preference multi-objective optimization. Journal of Software, 21(1), 14–33.MathSciNetMATHCrossRefGoogle Scholar
  13. 13.
    Luh, G. C., & Chueh, C. H. (2009). A multi-modal immune algorithm for the job-shop scheduling problem. Information Sciences, 179(10), 1516–1532.CrossRefGoogle Scholar
  14. 14.
    Chai, Z. Y., & Liu, F. (2010). Spectrum allocation of cognitive wireless network based on immune clone selection optimization. Journal on Communications, 31(11), 92–100.Google Scholar
  15. 15.
    Zhu, S. F., Liu, F., & Chai, Z. Y. (2011). Immune computing-based base station location planning in the TD-SCDMA network. Journal on Communications, 32(1), 106–110.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Fang Liu
    • 1
    • 2
  • Si-feng Zhu
    • 3
  • Zheng-yi Chai
    • 1
  • Yu-tao Qi
    • 1
    • 2
  • Jian-she Wu
    • 2
  1. 1.School of Computer Science and TechnologyXidian UniversityXi’anChina
  2. 2.Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of ChinaXidian UniversityXi’anChina
  3. 3.Department of Mathematics and Information ScienceZhoukou Normal UniversityZhoukouChina

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