BEGIN: Big Data Enabled Energy-Efficient Vehicular Edge Computing

Zhenyu Zhou, Chen Xu, Houjian Yu, Zheng Chang, Shahid Mumtaz, Jonathan Rodriguez

    Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

    Crynodeb

    Vehicular edge computing is essential to support future emerging multimedia-rich and delay-sensitive applications in vehicular networks. However, the massive deployment of edge computing infrastructures induces new problems including energy consumption and carbon pollution. This motivates us to develop BEGIN (Big data enabled EnerGy-efficient vehIcular edge computiNg), a programmable, scalable, and flexible framework for integrating big data analytics with vehicular edge computing. In this article, we first present a comprehensive literature review. Then the overall design principle of BEGIN is described with an emphasis on computing domain and data domain convergence. In the next section, we classify big data in BEGIN into four categories and then describe their features and potential values.
    Four typical application scenarios in BEGIN including node deployment, resource adaptation and workload allocation, energy management, and proactive caching and pushing, are provided to illustrate how to achieve energy-efficient vehicular edge computing by using big data. A case study is presented to demonstrate the feasibility of BEGIN and the superiority of big data in energy efficiency improvement. Finally, we conclude this
    work and outline future research open issues.
    Iaith wreiddiolSaesneg
    Tudalennau (o-i)82-89
    CyfnodolynIEEE Communications Magazine
    Cyfrol56
    Rhif cyhoeddi12
    Dynodwyr Gwrthrych Digidol (DOIs)
    StatwsCyhoeddwyd - 1 Rhag 2018

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