A context-aware edge-based VANET communication scheme for ITS

Chang An (Corresponding Author), Celimuge Wu (Corresponding Author), Tsutomu Yoshinaga, Xianfu Chen, Yusheng Ji

    Research output: Contribution to journalArticleResearchpeer-review

    Abstract

    We propose a context-aware edge-based packet forwarding scheme for vehicular networks. The proposed scheme employs a fuzzy logic-based edge node selection protocol to find the best edge nodes in a decentralized manner, which can achieve an efficient use of wireless resources by conducting packet forwarding through edges. A reinforcement learning algorithm is used to optimize the last two-hop communications in order to improve the adaptiveness of the communication routes. The proposed scheme selects different edge nodes for different types of communications with different context information such as connection-dependency (connection-dependent or connection-independent), communication type (unicast or broadcast), and packet payload size. We launch extensive simulations to evaluate the proposed scheme by comparing with existing broadcast protocols and unicast protocols for various network conditions and traffic patterns.

    LanguageEnglish
    Article number2022
    JournalSensors
    Volume18
    Issue number7
    DOIs
    Publication statusPublished - 1 Jul 2018

    Fingerprint

    communication
    Communication
    Network protocols
    Fuzzy Logic
    Humulus
    Reinforcement learning
    Learning algorithms
    Fuzzy logic
    reinforcement
    payloads
    learning
    traffic
    Learning
    logic
    resources
    routes
    conduction
    simulation

    Keywords

    • Broadcast communications
    • Context-aware communications
    • Edge computing
    • Intelligent transportation systems
    • Unicast communications
    • VANET
    • Vehicular networks

    OKM Publication Types

    • A1 Refereed journal article

    OKM Open Access Status

    • 1 Open Access

    ASJC Scopus subject areas

    • Analytical Chemistry
    • Atomic and Molecular Physics, and Optics
    • Biochemistry
    • Instrumentation
    • Electrical and Electronic Engineering

    Cite this

    An, Chang ; Wu, Celimuge ; Yoshinaga, Tsutomu ; Chen, Xianfu ; Ji, Yusheng. / A context-aware edge-based VANET communication scheme for ITS. In: Sensors. 2018 ; Vol. 18, No. 7.
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    abstract = "We propose a context-aware edge-based packet forwarding scheme for vehicular networks. The proposed scheme employs a fuzzy logic-based edge node selection protocol to find the best edge nodes in a decentralized manner, which can achieve an efficient use of wireless resources by conducting packet forwarding through edges. A reinforcement learning algorithm is used to optimize the last two-hop communications in order to improve the adaptiveness of the communication routes. The proposed scheme selects different edge nodes for different types of communications with different context information such as connection-dependency (connection-dependent or connection-independent), communication type (unicast or broadcast), and packet payload size. We launch extensive simulations to evaluate the proposed scheme by comparing with existing broadcast protocols and unicast protocols for various network conditions and traffic patterns.",
    keywords = "Broadcast communications, Context-aware communications, Edge computing, Intelligent transportation systems, Unicast communications, VANET, Vehicular networks",
    author = "Chang An and Celimuge Wu and Tsutomu Yoshinaga and Xianfu Chen and Yusheng Ji",
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    A context-aware edge-based VANET communication scheme for ITS. / An, Chang (Corresponding Author); Wu, Celimuge (Corresponding Author); Yoshinaga, Tsutomu; Chen, Xianfu; Ji, Yusheng.

    In: Sensors, Vol. 18, No. 7, 2022, 01.07.2018.

    Research output: Contribution to journalArticleResearchpeer-review

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    AU - Wu, Celimuge

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    AU - Ji, Yusheng

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    AB - We propose a context-aware edge-based packet forwarding scheme for vehicular networks. The proposed scheme employs a fuzzy logic-based edge node selection protocol to find the best edge nodes in a decentralized manner, which can achieve an efficient use of wireless resources by conducting packet forwarding through edges. A reinforcement learning algorithm is used to optimize the last two-hop communications in order to improve the adaptiveness of the communication routes. The proposed scheme selects different edge nodes for different types of communications with different context information such as connection-dependency (connection-dependent or connection-independent), communication type (unicast or broadcast), and packet payload size. We launch extensive simulations to evaluate the proposed scheme by comparing with existing broadcast protocols and unicast protocols for various network conditions and traffic patterns.

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    KW - Edge computing

    KW - Intelligent transportation systems

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