Pervasive Location Management Using Genetic Algorithm


  • S. Thabasu Kannan Principal, Pannai College of Engineering and Technology, Sivagangai – 630 561, Tamil Nadu, India
  • N. Shakeela Research Scholar, PRIST University and Assistant Professor, Dept. of Computer Science, Bharathidasan University, Tamil Nadu, India



Location Updates, Location Paging, Mobility Pattern, Call To Mobility, Cells, Vicinity, Reporting Cell


In mobile environment, mobility plays an important role i.e. without mobility there won’t be any transaction in
mobile networks. A mobile user calls stir from anywhere on the network. To keep the mobile connected, the networks should keep track of the incoming mobile receptive system.Both network should be effective, efficient to identify the optimum path and faster to find the number of mobile users.This is called location management, which contains the above two things in an efficient and effective manner. Here location update means the process of tracking the mobile terminals and paging means to find the correct mobile terminals. The main aim of this paper is to compute the least location update cost in pervasive environment. Actually this paper is used to integrate the intelligence in mobile environment to identify the least location updation and paging cost. By use of this intelligence, the extraction of output and its level of accuracy are very high.Here intelligence is used to manage the location. Here we have proposed to implement the above two aspects by using various operators of genetic algorithm to solve the reporting cells planning problem because the solution space to be searched is huge and its popularity & robustness. And also the new version of mobility pattern is used to minimize the total cost and to balance the Location update and search Paging. In the new system one mobility pattern is maintained in each and every visited cell. If the number of pattern is increased then the movement weight is reduced and the updating cost and seeking cost is also reduced. Mobile terminals update their positions upon entering one of these reporting cells.In our previous paper, we have used network size of 4X4 for testing purpose and in conclusion, we have mentioned the 6X6 and 8X8 network size as future extension. In this paper we have implemented that extension work for number of generation are 500 and 1,000 for executing various existing algorithms like POFLA, UMP and MIPN. Comparatively the new system is better than any other existing system we have mentioned. The main drawback here is same time taken for first call and maintain less time for subsequent calls only.


IanF.Akyildiz, JosephS.M.Ho and Yi-BingLin, “Movementbased location update and selective paging for pcs networks", IEEE/ACM Transactionson Networking,vol.4,no.4,pp.629-638, 2012.

S.Thabasu Kannan and Mrs.N.Shakeela, “Optimization of Location Management Cost by Mobility Pattern”, International Journal of Innovative Research in Advanced Engineering (IJIRAE), Issue 1, Volume 2 (January 2015).

S.Thabasu Kannan and Mrs.N.Shakeela, “Location Management Cost reduction by using Mobility Pattern”, Asian Journal of Electrical Sciences,Volume 3; Number 2; July - December 2014;Pages 46-50

P.G.Escalle, V.C.Giner and J.M.Oltra, “Reducing location update and paging costs in a pcs network", IEEE Transactionson Wireless communications, vol.1, no.1, pp.200-209,2012.

Z.Naor and H.Levy, “Minimizing the wireless cost of tracking mobile users: an adaptive threshold scheme", Seventeenth Annual Joint Conference of the IEEE Computer and Communications Societies, vol.2, pp.720-727,2012. [6] G.P.Pollini and C.-L.I, “A Profile-Based Location Strategy and Its Performance", IEEE JSAC, vol.15, no.8, pp.1415-1424,2013.

S.K.Sen, A.Bhattacharya and S.K.Das, “A Selective Location Update Strategy for PCS Users", ACM/Baltzer Journal of Wireless Networks, vol.5,no.5, pp.313-326,2012.

S.Tabbane, “Location management methods for third generation mobile systems", Communications Magazine, IEEE,vol.35, no.8, pp.72-84,2013.

VincentW.-S.Wong and Victor C.M.Leung, “Location Management for Next-Generation Personal Communications Networks", IEEE network, vol.14, No.5, pp.18-24,2013.

B.R.Badrinath_ T.Imielinski_ A. Virmani, “Locating Strategies for Personal Communication Networks”, Proc, IEEE GLOBECOM Workshop on Networking of Personal Communication, December 2009.

H.Kessler, “Mobile Users in Wireless Communication Networks”, Proc. IEEE INFOCOM, pp 45-50, May 2010

B. Awerbuch and D. Peleg, “Online Tracking of Mobile Users”, Journal of the Association for Computing Machinery, 42(5):1021{1058, September 1995.

Bar-Noy and I. Kessler, “Tracking Mobile Users in Wireless Communication Networks”, In Proceedings of IEEE INFOCOM, pages 1232 - 1239, 2013.

S. Rajagopalan and B. R. Badrinath, “An Adaptive Location Management Strategy for Mobile IP”, In Proceedings of First ACM Mobicom, 2013.




How to Cite

Thabasu Kannan, S., & Shakeela, N. (2015). Pervasive Location Management Using Genetic Algorithm. Asian Journal of Computer Science and Technology, 4(1), 1–7.