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Towards Knowledge-Driven Mobility Support

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Artificial Intelligence for Communications and Networks (AICON 2020)

Abstract

Mobility refers to the ability to conduct “seamless” communication with network entities whose network location constantly changes. This paper examines the mobility support problem in IP and Named Data Networking (NDN), and identifies two dimensions in the mobility support solution space: the host dimension and data dimension. Existing host dimension solutions have exhausted the available design choices, and have not been able to achieve new breakthroughs in performance. Recognizing this limitation, this paper proposes a novel knowledge dimension. In the knowledge dimension, two knowledge-driven mobility support approaches, Topology-driven Intermediate Placement (TIP) and Trajectory-driven Reachability Update (TRU), are proposed. These approaches exploit knowledge such as network topology and movement trajectory to tweak the network and network services for better overall mobility support performance. A cross-architectural quantitative evaluation framework covering two communication scenarios and 5 quantifiable metrics is proposed to evaluate mobility support performance. Experiment results show that the knowledge-driven approaches significantly improve mobility support performance, demonstrating the potential of the knowledge-driven vision for providing better mobility support.

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Authors and Affiliations

  1. Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China

    Zhongda Xia & Yu Zhang

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  1. Zhongda Xia
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  2. Yu Zhang
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Correspondence to Yu Zhang .

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Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Shuo Shi

  2. Harbin Institute of Technology, Harbin, China

    Liang Ye

  3. Harbin Institute of Technology, Harbin, China

    Yu Zhang

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Xia, Z., Zhang, Y. (2021). Towards Knowledge-Driven Mobility Support. In: Shi, S., Ye, L., Zhang, Y. (eds) Artificial Intelligence for Communications and Networks. AICON 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 356. Springer, Cham. https://doi.org/10.1007/978-3-030-69066-3_18

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  • DOI: https://doi.org/10.1007/978-3-030-69066-3_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69065-6

  • Online ISBN: 978-3-030-69066-3

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