The rapid development of the Internet of Vehicles (IoV) and intelligent transportation systems has led to increased demand for real-time data processing and computation in vehicular networks. To address these needs, this paper proposes a task offloading framework for UAV-assisted Vehicular Edge Computing (VEC) systems, which considers the high mobility of vehicles and the limited coverage and computational capacities of drones. We introduce the Mobility-Aware Vehicular Task Offloading (MAVTO) algorithm, designed to optimize task offloading decisions, manage resource allocation, and predict vehicle positions for seamless offloading. MAVTO leverages container-based virtualization for efficient computation, offering flexibility in resource allocation in multiple offload modes: direct, predictive, and hybrid. Extensive experiments using real-world vehicular data demonstrate that the MAVTO algorithm significantly outperforms other methods in terms of task completion success rate, especially under varying task data volumes and deadlines.


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    Title :

    Mobility-Aware Task Offloading and Resource Allocation in UAV-Assisted Vehicular Edge Computing Networks


    Contributors:
    Long Chen (author) / Jiaqi Du (author) / Xia Zhu (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown