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Mobile User Tracking Using A Hybrid Neural Network


In this paper, a novel technique for location prediction of mobile users has been proposed, and a paging technique based on this predicted location is developed. As a mobile user always travels with a destination in mind, the movements of users, are, in general, preplanned, and are highly dependent on the individual characteristics. Hence, neural networks with its learning and generalization ability may act as a suitable tool to predict the location of a terminal provided it is trained appropriately by the personal mobility profile of individual user. For prediction, the performance of a multi-layer perceptron (MLP) network has been studied first. Next, to recognize the inherent clusters in the input data, and to process it accordingly, a hybrid network composed of a self-organizing feature map (SOFM) network followed by a number of MLP networks has been employed. Simulation studies show that the latter performs better for location management. This approach is free from all unrealistic assumptions about the movement of the users. It is applicable to any arbitrary cell architecture. It attempts to reduce the total location management cost and paging delay, in general.

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  1. I.F. Akyildiz and J.S.M. Ho, Movement-based location update and selective paging for PCS networks, IEEE/ACM Transactions on Networking 4(4) (1996) 629–638.

    Article  Google Scholar 

  2. I.F. Akyildiz and J.S.M. Ho, Dynamic mobile user location update for wireless PCS networks, Wireless Networks 1(2) (1995) 187–196.

    Article  Google Scholar 

  3. B.R. Badrinath and T. Imielinski, Location management for networks with mobile users, in: Mobile Computing, T. Imielinski and H.F. Korth eds., (Kluwer Academic Publishers, 1996) 129–152.

  4. A. Barnoy, I. Kessler and M. Sidi, Mobile users: To update or not to update? Wireless Networks 1(2) (1995) 175–185.

    Article  Google Scholar 

  5. A. Bhattacharya and S.K. Das, Lezi-Update: An information- Theoretic approach to track mobile users in PCS networks, Proc. ACM/IEEE Mobicom ‘99, Seattle, WA 5(5) (1999) 1–12.

  6. Y. Birk and Y. Nachman, Using direction and elapsed-time information to reduce the wireless cost of locating mobile units in cellular networks, Wireless Networks 1(4) (1995) 403–412.

    Google Scholar 

  7. T.X. Brown and S. Mohan, Mobility management for personal communication systems, IEEE Transactions on Vehicular Technology 46(2) (1997) 269–278.

    Article  Google Scholar 

  8. G. Chakrabarty, Efficient Location Management by Movement Prediction of Mobile Host, in: Proc. Int. Workshop on Distributed Computing IWDC 2002, Lecture Notes in CS, Vol. 2571 (Springer 2002) pp. 142–153.

  9. S. Haykin, Neural Networks: A Comprehensive Foundation (McMillan College Publishing Co., New York, 1994).

    Google Scholar 

  10. J.S.M. Ho and I.F. Akyildiz, Mobile user location update and paging under delay constraints, ACM-Baltzer J. Wireless Networks 1(4) (1995) 413–425.

    Google Scholar 

  11. D. Hong and S.S. Rappaport, Traffic model and performance analysis for cellular radio telephone systems with prioritized and nonprioritized hand-off procedures, IEEE Transactions on Vehicular Tech. 42 (1993).

  12. K. Ivanov and G. Spring, Mobile speed sensitive handover in mixed cell environment, in: Proc. IEEE Vehicular Tech. Conference (1995) pp. 892–896.

  13. S.J. Kim and C.Y. Lee, Modeling and analysis of the dynamic location registration and paging in cellular systems, IEEE Transactions on Vehicular Technology 45(1) (1996) 82–90.

    Article  Google Scholar 

  14. T. Kohonen, The self-organization map, Proc. IEEE 78(9) (1990) 1464–1480.

    Article  Google Scholar 

  15. J. Li, H. Kameda and K. Li, Optimal dynamic mobility management for PCS networks, IEEE/ACM Trans. on Networking 8 (2000) 319–327.

    Article  Google Scholar 

  16. T. Liu, P. Bahl and I. Chlamtac, Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks, IEEE Transactions on Selected Areas of Commn. 16 (1998) 922–936.

    Article  Google Scholar 

  17. G.L. Lyberopoulos, J.G. Markoulidakis, D.V. Polymeros, D.F. Tsirkas and E.D. Sykas, Intelligent paging strategies for third generation mobile telecommunication systems, IEEE Transactions on Vehicular Technology 44(3) (1995) 543–553.

    Article  Google Scholar 

  18. U. Madhow, M. Honig and K. Steiglitz, Optimization of wireless resources for personal communications mobility tracking, IEEE/ACM Trans. on Networking 3 (1996) 629–638.

    Google Scholar 

  19. S. Pal, J. Das and K. Majumdar, A Hybrid Neural Architecture and its Application to Temperature Prediction, in: Proc. Joint Int. Conf. ICANN/ICONIP, 2003, Lecture Notes in CS Vol. 2714 (Springer 2003), pp. 581–588.

  20. G. Pollini and C.-L. I, A profile-based location strategy and its performance, IEEE Transactions on Selected Areas of Commn. 15(8) (1997).

  21. C. Rose, Minimizing the average cost of paging and registration: A timer-based method, Wireless Networks 2(2) (1996) 109–116.

    Article  Google Scholar 

  22. C. Rose and R. Yates, Minimizing the average cost of paging under delay constraints, Wireless Networks 1(2) (1995) 211–219.

    Article  Google Scholar 

  23. S. Tabbane, An alternative strategy for location tracking, IEEE Transactions on Selected Areas of Commn. 13 (1995) 880–892.

    Article  Google Scholar 

  24. V.W.S. Wong and V.C.M. Leung, An adptive distance-based location update algorithm for next-generation PCS networks, IEEE Transactions on Vehicular Technology 19(10) (2001) 1942–1952.

    Google Scholar 

  25. M. Zonoozi and P. Dassanayake, User mobility modeling and characterization of mobility patterns, IEEE Transactions on Selected Areas of Commn. 15 (1997) 1239–1252.

    Article  Google Scholar 

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Correspondence to Kausik Majumdar.

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Kausik Majumdar Received the B.E. degree in Electronics & Telecommunication from Jadavpur University, Kolkata in 2003. He is presently studying for M.Tech. degree in Optoelectronics & Optical Communication from IIT Delhi. Research interests include optical communication, computer networks, semiconductor devices and neural networks.

Nabanita Das received the B.Sc. (Hons.) degree in Physics in 1976, B.Tech. in Radio Physics and Electronics in 1979, from the University of Calcutta, the M.E. degree in Electronics and Telecommunication Engineering in 1981, and Ph.D in Computer Science in 1992, from Jadavpur University, Kolkata. Since 1986, she has been on the faculty of the Advanced Computing and Microelectronics unit, Indian Statistical Institute, Calcutta. She visited the department of Mathematik and Informatik, University of Paderborn, Germany, under INSA scientists’ exchange programme. She has co-authored many papers published in International journals of repute. She has acted as the co-guest Editor of the special issue on ‘Resource Management in mobile, ad hoc and sensor networks’ of Microprocessors and Microsystems, by Elsevier. She has acted as program chair of International workshop on distributed computing, IWDC 2004, and also as co-editor of the proceedings to be published as LNCS by Springer. Her research interests include parallel processing, interconnection networks, wireless communication and mobile computing. She is a senior member of IEEE.

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Majumdar, K., Das, N. Mobile User Tracking Using A Hybrid Neural Network. Wireless Netw 11, 275–284 (2005).

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  • mobile users
  • location management
  • paging
  • location prediction
  • MLP network
  • SOFM network