Scheduling of Digital Twin Synchronization in Industrial Internet of Things: A Hybrid Inverse Reinforcement Learning Approach

The digital transformation of industrial systems has been significantly influenced by the emergence of the Industrial Internet of Things. Digital twin (DT) technology plays a pivotal role in the transformation, serving as a bridge between the physical and digital realms. To support the efficient application of DT technology, the synchronization between physical entities (PEs) and DT models (DTMs) can not be ignored. Given the open nature of wireless channels, the diversity in synchronization mechanisms arising from the functional variations among PEs inevitably deteriorates the design of synchronization strategies. Moreover, a metric is required to gauge the synchronization between PEs and DTMs. In this article, a reinforcement learning (RL)-based online scheduling scheme is proposed to achieve efficient synchronization between DTMs and PEs with different mechanisms. Specifically, the Age of Information (AoI) is introduced as a metric to evaluate synchronization strategies. In addition, PEs are classified and share spectrum resources according to synchronous mechanisms, improving resource utilization efficiency. To ensure real-time scheduling, we propose a hybrid inverse RL-based scheme to support distributed time slot-level synchronization, reducing the need for manual intervention. Simulation results show that compared with other baseline RL schemes, the proposed scheme can reduce the AoI value more than 20% between the PE and the DTM.