Summary of A Learning-based Incentive Mechanism For Mobile Aigc Service in Decentralized Internet Of Vehicles, by Jiani Fan et al.
A Learning-based Incentive Mechanism for Mobile AIGC Service in Decentralized Internet of Vehicles
by Jiani Fan, Minrui Xu, Ziyao Liu, Huanyi Ye, Chaojie Gu, Dusit Niyato, Kwok-Yan Lam
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed decentralized incentive mechanism for mobile AI-generated content (AIGC) service allocation in the Internet of Vehicles (IoV) network utilizes multi-agent deep reinforcement learning to balance supply and demand, optimizing user experience and minimizing transmission latency. This approach outperforms baseline models, enhancing network efficiency, reconfigurability, data security, and privacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers propose a new way to allocate AI-generated content services on roadside units (RSUs) in the IoV network. Traditional cloud-based services are not suitable for this task because they require too many resources. The proposed method uses AI to find the best balance between what’s available and what users need, resulting in better user experience and faster data transfer. |
Keywords
» Artificial intelligence » Reinforcement learning
