With the rapid advancement of the Internet of Vehicles and artificial intelligence (AI) technologies, the cooperative intelligent transportation system (C-ITS) has drawn great attention in recent years. To provide an ultra-reliable, low-latency computation experience of C-ITS, computation offloading is deemed indispensable by working with edge-cloud servers. In this paper, we first investigate a distributed dynamic computation offloading model for multi-access edge computing (MEC) enabled C-ITS under a heterogeneous road network, in which the multiple and heterogeneous computing power sources cooperatively provide computation offloading services for vehicles. Considering the autonomous offloading manner of the vehicles, we formulate the task offloading and computing power allocation as a distributed Stackelberg game, where the MEC servers as the leader to allocate computing resources and manage local energy, and the vehicles as the followers to offload local computation task. Since the observable states in the game is incomplete, the problem of resolving the optimal strategies for each game player is modeled as a partially observable Markov decision process (POMDP) to maximize the long-term cumulative reward. Then we develop a computation offloading algorithm using Stackelberg game-based multi-agent deep deterministic policy gradient (SG-MADDPG), which uses a centralized training and decentralized execution method to learn the optimal computing power allocation and computation offloading policies. Finally, extensive simulations are carried out and show the rationality and effectiveness of the proposed algorithm.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Distributed Offloading for Cooperative Intelligent Transportation Under Heterogeneous Networks


    Beteiligte:
    Xia, Shichao (Autor:in) / Yao, Zhixiu (Autor:in) / Wu, Guangfu (Autor:in) / Li, Yun (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2022-09-01


    Format / Umfang :

    1953119 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Intelligent Task Offloading for Heterogeneous V2X Communications

    Xiong, Kai / Leng, Supeng / Huang, Chongwen et al. | IEEE | 2021



    Distributed Offloading for Multi-UAV Swarms in MEC-Assisted 5G Heterogeneous Networks

    Mingfang Ma / Zhengming Wang | DOAJ | 2023

    Freier Zugriff

    User Satisfaction-Aware WiFi Offloading in Heterogeneous Networks

    Xu, Jiao / Wu, Shaohua / Xu, Luyao et al. | IEEE | 2017


    INTELLIGENT TASK OFFLOADING

    INAM RAFIA / HATA ALBERTO / RUGGERI FRANCO et al. | Europäisches Patentamt | 2024

    Freier Zugriff