Dynamic Digital Twin Update by Adaptive Model Splitting and Reliable Crowdsourcing Under Uncertain Data Distortions

Aiming to provide high-fidelity and real-time virtual replicas, a digital twin (DT) model must be dynamically updated to precisely characterize the evolution of physical objects. Unlike the existing work, this paper studies a novel edge-cloud collaborative DT update framework with adaptive model splitting and reliable crowdsourcing under uncertain data distortions. Specifically, we consider that a global DT model can be split into arbitrary subsets of its elementary components (DT units), re-forming disjoint partial-DTs. Each partial-DT is constructed on distributed edge servers (ESs) by model training using the locally collected feature data. To enhance the system reliability, being more robust against uncertain data distortions that widely occur in practice, we further improve partial-DT constructions via crowdsourcing. In other words, each partial-DT is simultaneously trained by multiple ESs, i.e., an ES crowd, with one coordinator ES intermediately aggregating all models from participating ESs into a unified one. Then, the cloud collects and integrates partial-DTs from ES crowds to update the global DT. We formulate an online joint optimization problem to adaptively determine partial-DT splitting and ES crowdsourcing across different DT evolution periods or frames, with the objective of maximizing the long-term physical-virtual mapping accuracy. To this end, we first study a simplified short-term problem in each frame, modeled as a Bayesian coalition formation game (BCFG). We then develop an uncertainty-aware crowd formation algorithm based on a particularly established believe function to solve the BCFG for short-term optimal partial-DT assignment and coordinator ES selection, given any partial-DT splitting decisions. Moreover, we modify the BCFG to accommodate dynamic settings and design a deep reinforcement learning-based algorithm integrated with this modified BCFG, called DBC. The DBC algorithm extends the short-term solution to a long-term one, which jointly and dynamically optimizes partial-DT splitting and ES crowdsourcing, thereby addressing the original problem. Simulations show the effectiveness of the introduced dynamic DT update framework, and demonstrate the superiority of the proposed DBC algorithm over counterparts in terms of increasing the average DT update accuracy while reducing the associated costs.