Pervasive Location Management Using Genetic AlgorithmAuthor : S.Thabasu Kannan and N.Shakeela
Volume 4 No.1 January-June 2015 pp 1-7
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.
Location Updates, Location Paging, Mobility Pattern, Call To Mobility, Cells, Vicinity, Reporting Cell