Future Connected and Automated Vehicles (CAVs) will be supervised by cloud-based systems overseeing the overall security and orchestrating traffic flows. Such systems rely on data collected from CAVs across the whole city operational area. This paper develops a Fog Computing-based infrastructure for future Intelligent Transportation Systems (ITSs) enabling an agile and reliable off-load of CAV data. Since CAVs are expected to generate large quantities of data, it is not feasible to assume data off-loading to be completed while a CAV is in the proximity of a single Road-Side Unit (RSU). CAVs are expected to be in the range of an RSU only for a limited amount of time, necessitating data reconciliation across different RSUs, if traditional approaches to data off-load were to be used. To this end, this paper proposes an agile Fog Computing infrastructure, which interconnects all the RSUs so that the data reconciliation is solved efficiently as a by-product of deploying the Random Linear Network Coding (RLNC) technique. Our numerical results confirm the feasibility of our solution and show its effectiveness when operated in a large-scale urban testbed.


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    Titel :

    Agile Data Offloading over Novel Fog Computing Infrastructure for CAVs


    Beteiligte:
    Tassi, Andrea (Autor:in) / Mavromatis, Ioannis (Autor:in) / Piechocki, Robert (Autor:in) / Nix, Andrew (Autor:in) / Compton, Christian (Autor:in) / Poole, Tracey (Autor:in) / Schuster, Wolfgang (Autor:in)


    Erscheinungsdatum :

    01.04.2019


    Format / Umfang :

    3569532 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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