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Clustering techniques for dynamic mobility management

Published: 02 October 2006 Publication History

Abstract

This paper presents a new approach based on clustering algorithms in combination with the location area scheme to solve the mobile location management problem. Mobile users' past movement patterns are used in making future paging decisions by the network. This approach integrates the location area scheme and efficient clustering algorithms to find a network topology which can lead to massive savings in the number of signals made to locate users in the network. The proposed algorithm shows its advantages to the currently implemented GSM standards. The results provide new insights into the mobility management problem.

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Cited By

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  • (2020)Artificial intelligence‐based novel scheme for location area planning in cellular networksComputational Intelligence10.1111/coin.1237137:3(1338-1354)Online publication date: 15-Jul-2020
  • (2014)Solving the location areas management problem with multi-objective evolutionary strategiesWireless Networks10.1007/s11276-014-0718-x20:7(1909-1924)Online publication date: 1-Oct-2014
  • (2011)Soft computing approach for location management problem in wireless mobile environmentProceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part II10.1007/978-3-642-27242-4_29(248-256)Online publication date: 19-Dec-2011
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cover image ACM Conferences
MobiWac '06: Proceedings of the 4th ACM international workshop on Mobility management and wireless access
October 2006
206 pages
ISBN:159593488X
DOI:10.1145/1164783
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 02 October 2006

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Author Tags

  1. GSM network
  2. data clustering
  3. dynamic location management

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MobiWac '06 Paper Acceptance Rate 18 of 60 submissions, 30%;
Overall Acceptance Rate 83 of 272 submissions, 31%

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Cited By

View all
  • (2020)Artificial intelligence‐based novel scheme for location area planning in cellular networksComputational Intelligence10.1111/coin.1237137:3(1338-1354)Online publication date: 15-Jul-2020
  • (2014)Solving the location areas management problem with multi-objective evolutionary strategiesWireless Networks10.1007/s11276-014-0718-x20:7(1909-1924)Online publication date: 1-Oct-2014
  • (2011)Soft computing approach for location management problem in wireless mobile environmentProceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part II10.1007/978-3-642-27242-4_29(248-256)Online publication date: 19-Dec-2011
  • (2008)Bio-inspired Algorithms for Mobility ManagementProceedings of the The International Symposium on Parallel Architectures, Algorithms, and Networks10.1109/I-SPAN.2008.26(216-223)Online publication date: 7-May-2008

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