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Mobility Prediction for Dynamic Location Area in Cellular Network Using Hidden Markov Model

  • Nilesh B. PrajapatiEmail author
  • D. R. Kathiriya
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 11)

Abstract

To provide good quality of services to Mobile Users (MU) is main aim of every Cellular network. Radio bandwidth is critical resources which should be used optimally. More bandwidth is consumed due to frequent Location Update and paging. So, if we know the location of Mobile users in advance Location update and more paging can be reduced. In this paper, we implemented HMM method to predict current location of mobile user in Cellular network based on their previous mobility pattern and behavior. By the implementation, results shows that based on previous state of mobile user we can able to predict its current location. If previous state history is more than chance of getting accurate location of mobile user is higher.

Keywords

Mobile users Dynamic Location Area (DLA) Cellular Network Hidden Markov Model (HMM) Location Update (LU) Paging 

References

  1. 1.
    Mudaliar, K., Swadas, P., Prajapati, N.: Location management for cellular network using ant colony optimization. In: International Conference on Recent Trends on Computer Technology in Academia, 21–23 Apr 2012Google Scholar
  2. 2.
    Mudaliar, K., Prajapati, N.: User based personalization Scheme for dynamic location management. In: International Conference “ICNICT 11” at Karpagam University, Coimbatore, 15–16 Dec 2011Google Scholar
  3. 3.
    Lee, G., Chen, A.L.P.: The design of location regions using user movement behaviors in PCS systems. Multimedia Tools Appl. 15(2), 187–202 (2001)Google Scholar
  4. 4.
    Markande, S.D., Bodhe, S.K.: Cartesian coordinate system based dynamic location management scheme. Int. J. Electron. Eng. Res. 2 (2009)Google Scholar
  5. 5.
    Sricharan, M.S., Vaidehi, V.: A dynamic distance based location management strategy utilizing user profiles for next generation wireless networks. In: First International Conference on Industrial and Information Systems, ICIIS2006, 8–11 Aug 2006, Sri LankaGoogle Scholar
  6. 6.
    Vijay Kumar, B.P., Venkataram, P.: Prediction-based location management using multilayer neural networks. Indian Inst. Sci. 82, 7–21 (2002). © Indian Institute of ScienceGoogle Scholar
  7. 7.
    Singh, J.A.P., Karnan, M.: A dynamic location management scheme for wireless networks using cascaded correlation neural network. Int. J. Comput. Theory Eng. 2 (2010)Google Scholar
  8. 8.
    Zheng, J., Regentova, E., Srimani, P.K.: Dynamic location management with personalized location area of future PCS network. In: 6th International Workshop on Distributed Computing IWDC 2004, IndiaGoogle Scholar
  9. 9.
    Aoudjit, R., Belkadi, M., Daoui, M., Chamek, L.: Mobility prediction based on data mining. Int. J. Database Theory Appl. 6(2) (2013)Google Scholar
  10. 10.
    Kouemou, G.L.: History and Theoretical Basics of Hidden Markov Models. http://www.intechopen.com
  11. 11.
    Crawdad: Wireless Traces from Dartmouth. http://crawdad.cs.dratmouth.edu/
  12. 12.
    Rong, C., Senmiao, Y.: Distributed and Dynamic Location Area for PCS. IEEE, 1-4244-0463-0/06Google Scholar
  13. 13.
    Xie, H., Tabbane, S., Goodman, D.J.: Dynamic location area management and performance analysis. In: Proceeding of the 43rd IEEE Vehicular Technology ConferenceGoogle Scholar
  14. 14.
    Landfeldt, B., Kolodziej, N.: A dynamic location management scheme based on individual metrics and coordinates. In: Proceedings of IEEE WITSP04, Adelaide, Australia, Dec 2004Google Scholar
  15. 15.
    Scourias, J.: Dynamic Location Management and Activity-Based Mobility Modelling for Cellular Networks. Waterloo, Ontario, Canada, 1997 © John Scourias (1997)Google Scholar
  16. 16.
    Foughali, L., Talbi, E.-G.: A Parallel Insular Model for Location Area Planning in Mobile Networks. IEEE, 978-1-4244-1694-3 (2008)Google Scholar
  17. 17.
    Pierre, S., Houeto, F.: Assigning cells to switches in cellular mobile network using taboo search. IEEE Trans. Syst. 32(3), 351–356 (2002)Google Scholar
  18. 18.
    Akyildiz, I.F., McNair, J., Ho, J., Wang, W.: Mobility Management in Next Generation Wireless Systems. Broadband and Wireless Networking Laboratory School of Electrical and Computer Engineering Georgia Institute of Technology, AtlantaGoogle Scholar
  19. 19.
    Munguia-Marcario, M., Munoz-Rodriguez, D., Molina, C.: Optimal adaptive location area design and inactive location area. In: Proceedings of 47th IEEE Vehicular Technology Conference, vol. 1, pp. 510–514 (1997)Google Scholar
  20. 20.
    lBejerano, Y., Smith, M.A., (Seffi) Naor, J., Immorlica, N.: Efficient location area planning for personal communication systems. IEEE/ACM Trans. Netw. 14(2), (2006)Google Scholar
  21. 21.
    Wan, G., Lin, E.: Cost reduction in location management using semi-realtime movement information. Baltzer J. (1998)Google Scholar
  22. 22.
    Scourias, J., Kunz, T.: A Dynamic Individualized Location Management Algorithm. Department of Computer Science, University of Waterloo. 0-7803-3871-5/97/$10.00 © 1997 IEEEGoogle Scholar
  23. 23.
    Giner, V.C.: State of the art in Location Management procedures. Information Society Technologies (IST)—6th Framework ProgrammeGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Computer IT EngineeringB.V.M. Engineering College, Gujarat Technological UniversityAnandIndia
  2. 2.Computer Center, Anand Agriculture UniversityAnandIndia

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