Abstract
Location management is an important and complex issue in mobile computing. Location management problem can be solved by partitioning the network into location areas such that the total cost, i.e., sum of handoff (update) cost and paging cost is minimum. Finding the optimal number of location areas and the corresponding configuration of the partitioned network is NP-complete problem. In this paper, we present two swarm intelligence algorithms namely genetic algorithm (GA) and artificial bee colony (ABC) to obtain minimum cost in the location management problem. We compare the performance of the swarm intelligence algorithms and the results show that ABC give better optimal solution to locate the optimal solution.
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References
Kim, S.S., Kim, I.H., Mani, V., Kim, H.J., Agarwal, D.P.: Partitioning of mobile network into location areas using ant colony optimization. ICIC Express Lett. Part B: Appl. 1(1), 39–44 (2010)
Subrata, R., Zomaya, A.Y.: A comparison of three artificial life techniques for reporting cell planning in mobile computing. IEEE Trans. parallel Distrib. Syst. 14(2), 142–153 (2003)
Bar, N.A., Kessler, I.: Tracking mobile users in wireless communications networks. IEEE Trans. Inf. Theory 39, 1877–1886 (1993)
Okasaka. S., Onoe, S., Yasuda, S., Maebara, A.: A new location updating method for digital cellular systems. In: Proceedings of 41st IEEE Vehicular Technology Conference (1991)
Plassmann, D.: Location management strategies for mobile cellular networks of 3rd generation. In: Proceedings of IEEE 44th Vehicular Technology Conference (1994)
Yeung, K.L., Yum, T.S.P.: A comparative study on location tracking strategies in cellular mobile radio systems. In: Proceedings of IEEE Global Telecommunication Conference (1995)
Gondim, P.R.L.: Genetic algorithms and the location area partitioning problem in cellular networks. In: Proceedings of IEEE 46th Vehicular Technology Conference (1996)
Taheri, J., Albert Y.Z.: A genetic algorithm for finding optimal location area configurations for mobility management. IEEE Conference on Local Computer Networks 30th Anniversary (2005)
Yannis, M., Magdalene, M., Michael, D., Nikolaos, M., Constantin, Z.: A hybrid stochastic genetic-GRASP algorithm for clustering analysis. Oper. Res. Int. J. 8, 33–46 (2008). doi:10.1007/s12351-008-0004-8
Bejerano, Y., Smith, M.A., Naor, J.S., Immorlica, N.: Efficient location area planning for personal communication systems. IEEE/ACM Trans. Netw. 14, 438–450 (2006)
Subrata, R., Zomaya, A.Y.: Evolving cellular automata for location management in mobile computing networks. IEEE Trans. Parallel Distrib. Syst. 14, 13–26 (2003)
Imielinski, T., Badrinath, B.R.: Querying locations in wireless environments. In: Proceedings of Wireless Communication and Future Directions (1992)
Goldberg, D.E.: Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8(1), 687–697 (2008)
Senthilnath, J., Omkar, S.N., Mani, V., Tejovanth, N., Diwakar, P.G., Archana Shenoy, B.: Hierarchical clustering algorithm for land cover mapping using satellite images. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 5(3), 762–768 (2012)
Craig, D., Omkar, S.N., Senthilnath, J.: Pickup and delivery problem using metaheuristics. Expert Syst. Appl. 39(1), 328–334 (2012)
Senthilnath, J., Omkar, S.N., Mani, V.: Clustering using firefly algorithm: performance study. Swarm Evol. Comput. 1(3), 164–171 (2011)
Omkar, S.N., Senthilnath, J., Khandelwal, R., Narayana Naik, G., Gopalakrishnan, S.: Artificial Bee Colony (ABC) for multi-objective design optimization of composite structures. Appl. Soft Comput. 11(1), 489–499 (2011)
Omkar, S.N., Senthilnath, J.: Artificial bee colony for classification of acoustic emission signal sources. Int. J. Aerosp. Innov. 1(3), 129–143 (2009)
Omkar, S.N., Senthilnath, J., Suresh, S.: Mathematical model and rule extraction for tool wear monitoring problem using nature inspired techniques. Indian J. Eng. Mater. Sci. 16, 205–210 (2009)
Omkar, S.N., Senthilnath, J.: Mudigere, D., Manoj Kumar, M.: Crop classification using biologically inspired techniques with high resolution satellite image. J. Indian Soc. Remote Sens. 36(2), 172–182 (2008)
Omkar, S.N., Senthilnath J.: In: Dehuri, S., et al. (eds.) Integration of Swarm Intelligence and Artificial Neutral Network, Neural Network and Swarm Intelligence for Data Mining, chap. 2. World Scientific Press, Singapore, pp. 23–65 (2011)
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Goel, N., Senthilnath, J., Omkar, S.N., Mani, V. (2014). Location Management in Mobile Computing Using Swarm Intelligence Techniques. In: Babu, B., et al. Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012. Advances in Intelligent Systems and Computing, vol 236. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1602-5_51
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DOI: https://doi.org/10.1007/978-81-322-1602-5_51
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