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A Novel Genetic Algorithm Approach to Mobility Prediction in Wireless Networks

  • C. Mala
  • Mohanraj Loganathan
  • N. P. Gopalan
  • B. SivaSelvan
Part of the Communications in Computer and Information Science book series (CCIS, volume 40)

Abstract

Wireless networks are required to support nomadic computing, providing seamless mobility without call drops. The number of mobile users must be known apriori for allocating the channel bandwidth, which is a function of the number of mobile users already admitted into the system and direction of their movement. If the user’s next movement can be predicted, it may be used to allocate a channel in the neighboring cell, thereby reducing the call dropping rate and leading to seamless mobility. Most of the mobility prediction algorithms offer reduced performance as a result of being movement history based and also do not give accurate results when users move towards the corner of the hexagonal cell structure but for the sectorized ones. Substantial improvement is demonstrated with the use of the proposed novel genetic algorithm based approach presented in the paper. Simulation results show that the proposed algorithm gives accurate results independent of the user’s movement pattern.

Keywords

Seamless mobility Mobility prediction Channel allocation Genetic algorithm Call dropping rate HHO region 

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References

  1. 1.
    Chan, J., Seneviratne, A.: A practical user mobility prediction algorithm for supporting adaptive qos in wireless networks. In: IEEE International Conference ICON 1999, September 1999, pp. 104–111 (1999)Google Scholar
  2. 2.
    Yang, J., Jiang, Q., Manivannan, D.: A fault-tolerant distributed channel allocation scheme for cellular networks. IEEE Transactions on Computers 54(5), 616–629 (2005)CrossRefGoogle Scholar
  3. 3.
    Chellappa, R., Jennings, A., Shenoy, N.: The sectorized mobility prediction algorithm for wireless networks. In: International Conference on Information and Communication Technologies (ICT 2003) (April 2003)Google Scholar
  4. 4.
    Liu, T., Bahl, P., Chlamtac, I.: A hierarchical position prediction algorithm for efficient management of resources in cellular networks. In: Global Telecommunication conference, GLOBECOM 1997, November 1997, pp. 982–986 (1997)Google Scholar
  5. 5.
    Liu, G., Maguire Jr., G.: A class of mobile motion prediction algorithms for wireless mobile computing and communications. Mobile Networks and Applications 1(2), 113–121 (1996)CrossRefGoogle Scholar
  6. 6.
    Pathirana, P., Savkin, A., Jha, S.: Mobility modeling and trajectory prediction for cellular networks with mobile base station. In: Mobihoc 2003, Maryland, USA (June 2003)Google Scholar
  7. 7.
    Goldberg, D.E.: In: Genetic Algorithms in Search, Optimization, and Machine Learning. Prentice-Hall, Englewood Cliffs (2006)Google Scholar
  8. 8.
  9. 9.
    Dasilva, L.A.: Pricing for qos-enabled networks: a survey. IEEE Communication Surveys and Tutorials 3(2), 2–8 (2000)CrossRefGoogle Scholar
  10. 10.
    Das, S.K., Sen, S.K.: New location update strategy for cellular networks and its implementation using a genetic algorithm. In: MOBICOM 1997, Budapest (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • C. Mala
    • 1
  • Mohanraj Loganathan
    • 2
  • N. P. Gopalan
    • 3
  • B. SivaSelvan
    • 4
  1. 1.National Institute of TechnologyTiruchirappalliIndia
  2. 2.Department of Computer Science & EngineeringNational Institute of TechnologyTiruchirappalliIndia
  3. 3.Department of Computer ApplicationsNational Institute of TechnologyTiruchirapalliIndia
  4. 4.IIITDM KancheepuramChennaiIndia

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