An Intelligent Broadcast Protocol for VANETs Based on Transfer Learning

Celimuge Wu, Xianfu Chen, Yusheng Ji, Satoshi Ohzahata, Toshihiko Kato

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    15 Citations (Scopus)


    Designing an efficient multi-hop broadcast protocol is very important for the realization of collision avoidance systems and other many interesting applications in vehicular ad hoc networks (VANETs). Existing protocols are optimized for a specific scenario, and are not capable of working in various scenarios. Therefore, designing an intelligent protocol which can tune itself in relation to the change of network environment is particularly important. In this paper, we propose a broadcast protocol which is able to make forwarding decision based on a self-learning mechanism. The protocol employs a fuzzy logic-based relay node selection approach to take into account multiple metrics for the forwarding algorithm. The parameters used for the fuzzy logic are tuned online using a reinforcement learning approach. Transfer learning is used to transfer knowledge to new arriving vehicles (agents) in order to shorten the convergence time. The combination of reinforcement learning, transfer learning and fuzzy logic can provide an intelligent solution for broadcasting in VANETs. We conduct computer simulations to evaluate the proposed protocol.
    Original languageEnglish
    Title of host publicationVehicular Technology Conference (VTC Spring), 2015 IEEE 81st
    ISBN (Electronic)978-1-4799-8088-8
    Publication statusPublished - 2015
    MoE publication typeA4 Article in a conference publication
    Event81st Vehicular Technology Conference, VTC2015-Spring: VTC2015-Spring - Glasgow, Scotland, United Kingdom
    Duration: 11 May 201514 May 2015
    Conference number: 81


    Conference81st Vehicular Technology Conference, VTC2015-Spring
    Country/TerritoryUnited Kingdom
    CityGlasgow, Scotland


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