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A Content-Based Routing Scheme for Mobile Data Offloading in Pocket Switched Networks

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AI 2016: Advances in Artificial Intelligence (AI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9992))

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Abstract

Existing cellular network infrastructure will likely be compromised in the future by the increase of mobile users and mobile data services. Continuous improvements of network infrastructure and several mobile data offloading strategies are promising solutions for cellular providers to cope with the demand increase of mobile data. However, these options require a lot of cost and time to implement. Few studies have been conducted to assess whether Pocket Switched Network (PSN) can be applied to support mobile data offloading. In this paper, we present a PSN mobile data offloading scheme that utilizes mobile users with available connectivity to deliver content-aware data to other mobile users. This paper also aims to evaluate whether PSN routing schemes can be applied to improve the current strategies in mobile data offloading. The simulation study exhibits promising results in terms of message delivery and latency reduction.

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Acknowledgement

This work was supported by the National Foundation of Korea (NSF) grant funded by the Korea government (MSIP) (No. 2016R1A2B40130002, 2013054460).

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Authors and Affiliations

  1. College of Information and Communications Technology, West Visayas State University, Iloilo City, Philippines

    Regin Cabacas

  2. School of Computer, Information and Communication Engineering, Kunsan National University, Gunsan, South Korea

    In-Ho Ra

Authors
  1. Regin Cabacas
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  2. In-Ho Ra
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Correspondence to In-Ho Ra .

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Editors and Affiliations

  1. University of Tasmania, Hobart, Australia

    Byeong Ho Kang

  2. Auckland University of Technology, Auckland, New Zealand

    Quan Bai

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Cabacas, R., Ra, IH. (2016). A Content-Based Routing Scheme for Mobile Data Offloading in Pocket Switched Networks. In: Kang, B.H., Bai, Q. (eds) AI 2016: Advances in Artificial Intelligence. AI 2016. Lecture Notes in Computer Science(), vol 9992. Springer, Cham. https://doi.org/10.1007/978-3-319-50127-7_62

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  • DOI: https://doi.org/10.1007/978-3-319-50127-7_62

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50126-0

  • Online ISBN: 978-3-319-50127-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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