Recent developments in the Internet of Vehicles (IoV) enabled the myriad emergence of a plethora of data-intensive and latency-sensitive vehicular applications, posing significant difficulties to traditional cloud computing. Vehicular edge computing (VEC), as an emerging paradigm, enables the vehicles to utilize the resources of the edge servers to reduce the data transfer burden and computing stress. Although the utilization of VEC is a favourable support for IoV applications, vehicle mobility and other factors further complicate the challenge of designing and implementing such systems, leading to incremental delay and energy consumption. In recent times, there have been attempts to integrate deep reinforcement learning (DRL) approaches with IoV-based systems, to facilitate real-time decision-making and prediction. We demonstrate the potential of such an approach in this paper. Specifically, the dynamic computation offloading problem is constructed as a Markov decision process (MDP). Then, the twin delayed deep deterministic policy gradient (TD3) algorithm is utilized to achieve the optimal offloading strategy. Finally, findings from the simulation demonstrate the potential of our proposed approach.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Dynamic Edge Computation Offloading for Internet of Vehicles With Deep Reinforcement Learning


    Contributors:
    Yao, Liang (author) / Xu, Xiaolong (author) / Bilal, Muhammad (author) / Wang, Huihui (author)

    Published in:

    Publication date :

    2023-11-01


    Size :

    1551437 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English