Ground-breaking innovations in transport, such as autonomous vehicles, the European Local Dynamic Map (LDM) and related on-line services heavily depend on reliable vehicular connectivity. In the most likely scenario, hybrid vehicular networks will use the IEEE 802.11p protocol for vehicle-to-vehicle (V2V) communication, and the cellular network (e.g. LTE or 5G) as a gateway to remote servers. Both technologies have their own flaws as IEEE 802.11p available bandwidth drops quickly with the accretion of vehicles in the vicinity, and intensive cellular usage can be costly. In this context we intend to minimize this cost while ensuring system reliability. Clustering vehicles can significantly reduce cellular network usage when the Cluster Head (CH) is the only node that communicates with the cellular Base Station (BS) and also performs data aggregation. Other nodes communicate with the CH using multi-hop forwarding over IEEE 802.11p. There is however a tradeoff in cluster design. On the positive side, large clusters lead to high aggregation levels and thus low usage of the uplink cellular resources. On the negative side, large clusters also increase packet losses in the IEEE 802.11p network affecting communication reliability. In this paper, we study the impact of the number of communication hops on the average size of formed clusters, on data compression, and on IEEE 802.11p packet losses in various vehicle densities. We present a new clustering algorithm which delegates the CH election to the cellular BS, significantly improving the cluster formation compared to CH self-election algorithms. We propose a dynamic clustering approach that adapts the cluster size to the vehicle density and optimize data compression under the constraint of


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    Titel :

    Dynamic cluster size optimization in hybrid cellular-vehicular networks


    Beteiligte:


    Erscheinungsdatum :

    01.11.2016


    Format / Umfang :

    475406 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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