Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems

TL;DR

This paper proposes a sustainable incentive mechanism using diffusion-based algorithms for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems, addressing data sharing challenges and improving system efficiency.

Contribution

It introduces a novel GenAI-driven Digital Twin architecture and a contract theory model with a diffusion-based algorithm to solve adverse selection in data sharing.

Findings

Effective DT construction and updates demonstrated

Algorithm reduces parameter complexity for efficiency

Numerical results confirm scheme's effectiveness

Abstract

Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout product life cycles, 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 (GenAI) can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing. However, mechanisms that leverage Industrial Internet of Things (IIoT) devices to share sensing data for DT construction are susceptible to adverse selection problems. In this paper, we first develop a GenAI-driven DT architecture in ICPSs. To address the adverse selection problem caused by information asymmetry, we propose a contract theory model and develop a sustainable diffusion-based soft actor-critic algorithm to identify the optimal feasible contract. Specifically, we leverage dynamic structured pruning techniques to reduce parameter numbers of actor networks, allowing sustainability and efficient implementation of the proposed algorithm. Numerical results demonstrate the effectiveness of the proposed scheme and the algorithm, enabling efficient DT construction and updates to monitor and manage ICPSs.