Vehicular edge computing (VEC) tackles the escalating computational demands of intelligent transportation systems by offloading tasks to nearby roadside units (RSUs) for processing. However, in the dynamic vehicular network environment, where vehicles are constantly moving, effective VEC demands a sophisticated approach to managing computing, caching, and communication resources. This involves coordinating resource allocation and data caching across multiple vehicles and RSUs while making complex decisions about task placement. In this paper, we present VECO, a Vehicular Edge Caching and Offloading framework powered by digital twins (DTs). VECO leverages DTs for real-time monitoring of network conditions and resource states, enabling predictive analysis and intelligent decision-making. The framework incorporates a Dynamic Task Caching and Computation Offloading (DT2C) mechanism to optimize data caching and adapt task offloading based on task characteristics and dynamic resource availability. Specifically, we develop a utility-based caching algorithm for RSUs and a novel task offloading strategy using a Proximal Policy Optimization-based deep reinforcement learning algorithm. Extensive experiments demonstrate that VECO, augmented by the DT2C mechanism, significantly outperforms baseline approaches, achieving faster learning convergence and a 21% reduction in total costs, including system latency and energy consumption.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    VECO: A Digital Twin-Empowered Framework for Efficient Vehicular Edge Caching and Computation Offloading


    Contributors:
    Lin, Li (author) / Chen, Weijie (author) / He, Qiang (author) / Xiong, Jinbo (author) / Lin, Jiayin (author) / Lin, Limei (author)


    Publication date :

    2025-07-01


    Size :

    1839463 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Computation Offloading and Service Caching for Intelligent Transportation Systems With Digital Twin

    Xu, Xiaolong / Liu, Zhongjian / Bilal, Muhammad et al. | IEEE | 2022


    Edge Computation Offloading With Content Caching in 6G-Enabled IoV

    Zhou, Xuanhong / Bilal, Muhammad / Dou, Ruihan et al. | IEEE | 2024



    Cooperative Computation Offloading in Blockchain-Based Vehicular Edge Computing Networks

    Lang, Ping / Tian, Daxin / Duan, Xuting et al. | IEEE | 2022