We consider vehicular networking scenarios where existing vehicle-to-vehicle (V2V) links can be leveraged for an effective uploading of large-size data to the network. In particular, we consider a group of vehicles where one vehicle can be designated as the leader and other follower vehicles can offload their data to the leader vehicle or directly upload it to the base station (or a combination of the two). In our proposed framework, the leader vehicle is responsible to receive the data from other vehicles and to process it in order to remove the redundancy before uploading it to the base station. We present a mathematical framework of the considered network. Next, we formulate and solve an optimization problem for selecting the leader vehicle as well as determining the portions of data from other follower vehicles to be offloaded to the leader to take advantage of V2V links in the vehicular network. We also perform simulations to confirm our findings and to compare it with two other alternatives: (1) the follower vehicles offload all their data to the leader vehicle (who will then upload it to the network), and (2) the follower vehicles all upload it directly to the base station. Our numerical results show the superiority of the proposed approach in comparison with these alternatives.


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    Titel :

    Optimized Strategies for Big Data Offloading in Vehicular Ad-Hoc Networks


    Beteiligte:
    Akyildiz, Talha (Autor:in) / Zeng, Tengchan (Autor:in) / Lee, Yun Ho (Autor:in) / Tonshal, Basavaraj (Autor:in) / Mahdavifar, Hessam (Autor:in)


    Erscheinungsdatum :

    2023-06-01


    Format / Umfang :

    1779555 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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