On reducing paging cost in IP-based wireless/mobile networks

Kyoungae Kim, Sangheon Pack, Yanghee Choi

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Citations (Scopus)

Abstract

For more scalable mobile services, it is important to reduce the signaling cost for location management. In this paper, we propose a cost effective IP paging scheme utilizing explicit multicast (xcast). Xcast is a new kind of multicast scheme for small sized groups which uses unicast with low maintenance overhead. In terms of the paging algorithm, we use a selective paging scheme to minimize the paging cost, by dividing a paging area into several sub-paging areas. For the performance analysis, we develop an analytical model based on the random walk model in the mesh and hexagonal cell configurations. Using this model, we compared the paging cost between IP paging scheme using xcast and that using unicast or multicast. The results indicate that the proposed paging scheme reduces the paging cost by 44-84% compared with traditional paging schemes using multicast.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsHyun-Kook Kahng, Shigeki Goto
PublisherSpringer Verlag
Pages154-163
Number of pages10
ISBN (Print)3540230343
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3090
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Kim, K., Pack, S., & Choi, Y. (2004). On reducing paging cost in IP-based wireless/mobile networks. In H-K. Kahng, & S. Goto (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 154-163). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3090). Springer Verlag. https://doi.org/10.1007/978-3-540-25978-7_16