Stackelberg Game-Based Computation Task Offloading for Generative Digital Twins System in IIoT Environment

In Industrial Internet of Things (IIoT) environment, the deployment of the generative digital twin (GDT) system faces the complexity of real-time synchronization and objective conflicts between end-devices and edge gateways. Therefore, we propose a Stackelberg-based generative task offloading (STA-GTO) algorithm to address the deployment challenges of the GDT system. Specifically, we model the different objectives of end-devices and edge gateways in the GDT system as two optimization problems and develop the conditional temporal diffusion model (CTDM) to predict the future environmental disturbance states in the GDT system. Finally, the two optimization problems are transformed into a Stackelberg game problem, and we develop an offloading strategy generation algorithm based on dual-timescale multiagent proximal policy optimization (MAPPO), thereby obtaining a local approximate Stackelberg equilibrium solution. Experimental results demonstrate that the STA-GTO algorithm can effectively cope with environmental dynamics and outperforms the benchmark algorithms in both the total system utility and the synchronization latency of the GDT system.