DTAS: Adaptive Model Splitting for Dynamic Digital Twin Update with Edge-Cloud Collaboration

Digital Twin (DT) technology enables creating high-fidelity virtual models of physical objects, which must be dynamically updated to capture inherent evolutions. Conventional cloud-based schemes often suffer from excessive costs and limited data access, while updating the entire DT model on distributed edge servers (ESs) may cause inconsistency and incompleteness due to prevalent data silos. In response, we propose DTAS, an edge-cloud collaborative DT update framework via adaptive model splitting. In DTAS, the DT model is viewed as a global one composed by multiple elementary units, which are split into disjoint partial-DTs constructed on ESs using locally collected data, and then integrated by the cloud for update. To enhance reliability against uncertain data distortions, after splitting, we allow each partial-DT to be constructed via crowdsourcing, i.e., being built and aggregated by multiple ESs before uploading to the cloud. We formulate an online optimization problem to maximize the physical-virtual mapping accuracy in the dynamic DT update. By leveraging coalitional game, we first derive the solution for a simplified short-term problem, and then extend it adopting deep reinforcement learning to a long-term one, thereby solving the original problem. Simulations show that DTAS is not only effective for the dynamic DT update, but also superior over counterparts with an improvement of physical-virtual mapping accuracy by 25.76% on average.