Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems
<|reference_start|>Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems: Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout the product life cycle, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. Thanks to data process capability, Generative Artificial Intelligence (GAI) can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing. However, mechanisms that leverage sensing Industrial Internet of Things (IIoT) devices to share data for the construction of DTs are susceptible to adverse selection problems. In this paper, we first develop a GAI-driven DT architecture for ICPSs. To address the adverse selection problem caused by information asymmetry, we propose a contract theory model and develop the sustainable diffusion-based soft actor-critic algorithm to identify the optimal feasible contract. Specifically, we leverage the dynamic structured pruning technique to reduce parameter numbers of actor networks, allowing sustainability and efficient implementation of the proposed algorithm. Finally, numerical results demonstrate the effectiveness of the proposed scheme.<|reference_end|>
@article{wen2024sustainable,
    title={Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven
  Digital Twins in Industrial Cyber-Physical Systems},
    author={Jinbo Wen, Jiawen Kang, Dusit Niyato, Yang Zhang, and Shiwen Mao},
    journal={arXiv preprint arXiv:2408.01173},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01173},
    primaryClass={cs.NI cs.LG}
}