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dc.contributor.authorGoel, N.
dc.contributor.authorSenthilnath, J.
dc.contributor.authorOmkar, S.N.
dc.contributor.authorMani, V.
dc.identifier.citationAdvances in Intelligent Systems and Computing, 2014, Vol.236, , pp.481-489en_US
dc.description.abstractLocation 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. � Springer India 2014.en_US
dc.titleLocation management in mobile computing using swarm intelligence techniquesen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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