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Improvement of Routing in Opportunistic Communication Networks of Vehicles by Unsupervised Machine Learning

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Engineering Applications of Neural Networks (EANN 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1000))

Abstract

This paper deals with the problem of an application of machine learning in order to improve routing in a special class of opportunistic networks of vehicles called cluster opportunistic networks. We have proposed the hierarchical routing algorithm which combines three strategies in order to improve routing in OPNs: metric based on the node affiliation with detected OPN geographic sector, metrics based on the node affiliation with the communication community constructed in the spatio-temporal domain with time constraints and metric based on the node local encounter measure. The proposed routing scheme combines these four metrics to make decisions on message forwarding. The proposed routing method performance has been evaluated on simulation scenario and compared to Epidemic routing.

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Authors and Affiliations

  1. Faculty of Information Technology, Department of Digital Design, Czech Technical University in Prague, Prague, Czech Republic

    Ladislava Smítková Janků & Kateřina Hyniová

Authors
  1. Ladislava Smítková Janků
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  2. Kateřina Hyniová
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Corresponding author

Correspondence to Ladislava Smítková Janků .

Editor information

Editors and Affiliations

  1. David Goldman Informatics Centre, University of Sunderland, Sunderland, UK

    John Macintyre

  2. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  3. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  4. Oxford Brookes University, Oxford, UK

    Chrisina Jayne

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Smítková Janků, L., Hyniová, K. (2019). Improvement of Routing in Opportunistic Communication Networks of Vehicles by Unsupervised Machine Learning. In: Macintyre, J., Iliadis, L., Maglogiannis, I., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2019. Communications in Computer and Information Science, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-20257-6_35

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  • DOI: https://doi.org/10.1007/978-3-030-20257-6_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20256-9

  • Online ISBN: 978-3-030-20257-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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