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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2403.20151 |
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| _version_ | 1866909196516065280 |
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| author | Fan, Jiani Xu, Minrui Liu, Ziyao Ye, Huanyi Gu, Chaojie Niyato, Dusit Lam, Kwok-Yan |
| author_facet | Fan, Jiani Xu, Minrui Liu, Ziyao Ye, Huanyi Gu, Chaojie Niyato, Dusit Lam, Kwok-Yan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_20151 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Learning-based Incentive Mechanism for Mobile AIGC Service in Decentralized Internet of Vehicles Fan, Jiani Xu, Minrui Liu, Ziyao Ye, Huanyi Gu, Chaojie Niyato, Dusit Lam, Kwok-Yan Artificial Intelligence 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. |
| title | A Learning-based Incentive Mechanism for Mobile AIGC Service in Decentralized Internet of Vehicles |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2403.20151 |