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IP Mobility Performance Enhancement Using Link-Layer Prediction

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Future Generation Information Technology (FGIT 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6485))

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Abstract

In this paper, a prediction-based L2 Trigger approach is proposed for enhancing the performance of IP mobility in an integrated mobile Internet (e.g., Mobile WiMAX) and fast mobile IPv6 (FMIPv6) environment. The time series model of auto-regressive integrated moving average (ARIMA) is used to make short-term forecasting of mobile user’s signal strength. Through the forecast of the signal strength, layer-3 handover activities occur prior to the start of layer-2 handover process, and therefore, total handover latency as well as service disruption time can be reduced.

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References

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Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Kwangwoon University, 447-1, Wolgye-dong, Nowon-gu, Seoul, 139-701, Korea

    Jun-Hui Lee, Hyun-Woo Kim, Yong-Hoon Choi, Young-Uk Chung & Seung-Hyong Rhee

Authors
  1. Jun-Hui Lee
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  2. Hyun-Woo Kim
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  3. Yong-Hoon Choi
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  4. Young-Uk Chung
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  5. Seung-Hyong Rhee
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Editor information

Editors and Affiliations

  1. Hannam University, 133 Ojeong-dong, daeduk-gu, 306-791, Daejeon, South Korea

    Tai-hoon Kim

  2. Hannam University, Daejeon, South Korea

    Young-hoon Lee

  3. University of Tasmania, Hobart, Tasmania, Australia

    Byeong-Ho Kang

  4. University of Warsaw & Infobright Inc.,, Poland

    Dominik Ślęzak

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© 2010 Springer-Verlag Berlin Heidelberg

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Lee, JH., Kim, HW., Choi, YH., Chung, YU., Rhee, SH. (2010). IP Mobility Performance Enhancement Using Link-Layer Prediction. In: Kim, Th., Lee, Yh., Kang, BH., Ślęzak, D. (eds) Future Generation Information Technology. FGIT 2010. Lecture Notes in Computer Science, vol 6485. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17569-5_19

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  • DOI: https://doi.org/10.1007/978-3-642-17569-5_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17568-8

  • Online ISBN: 978-3-642-17569-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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