Artificial Intelligence-Generated Content (AIGC) refers to the paradigm of automated content generation utilizing AI models. Mobile AIGC services in the Internet of Vehicles (IoV) network have numerous advantages over traditional cloud-based AIGC services, including enhanced network efficiency, better reconfigurability, and stronger data security and privacy. Nonetheless, AIGC service provisioning frequently demands significant resources. Consequently, resource-constrained roadside units (RSUs) face challenges in maintaining a heterogeneous pool of AIGC services and addressing all user service requests without degrading overall performance. Therefore, in this paper, we propose a decentralized incentive mechanism for mobile AIGC service allocation, employing multi-agent deep reinforcement learning to find the balance between the supply of AIGC services on RSUs and user demand for services within the IoV context, optimizing user experience and minimizing transmission latency. Experimental results demonstrate that our approach achieves superior performance compared to other baseline models.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Learning-based Incentive Mechanism for Mobile AIGC Service in Decentralized Internet of Vehicles


    Contributors:
    Fan, Jiani (author) / Xu, Minrui (author) / Liu, Ziyao (author) / Ye, Huanyi (author) / Gu, Chaojie (author) / Niyato, Dusit (author) / Lam, Kwok-Yan (author)


    Publication date :

    2023-10-10


    Size :

    2268837 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Incentive Mechanism Design for Federated Learning in the Internet of Vehicles

    Lim, Wei Yang Bryan / Xiong, Zehui / Niyato, Dusit et al. | IEEE | 2020


    FedBeam: Reliable Incentive Mechanisms for Federated Learning in UAV-Enabled Internet of Vehicles

    Gangqiang Hu / Donglin Zhu / Jiaying Shen et al. | DOAJ | 2024

    Free access


    Ai-Enhanced Incentive Design for Crowdsourcing in Internet of Vehicles

    Yue, Yanlin / Sun, Wen / Liu, Jiajia et al. | IEEE | 2019


    Framework for Large-Scale Urban Traffic State Estimation Based on AIGC

    Lin, Hongyi / Liu, Jiahui / Qiu, Hanyi et al. | Springer Verlag | 2024