Intelligent Dual Time Scale Network Slicing for Sensory Information Synchronization in Industrial IoT Networks

Digital twins (DTs), as an effective technology for remote monitoring and management of devices, enhances the intelligence of the industrial Internet of Things (IIoT). Nonetheless, the unreliable and delayed transmission of sensory data in wireless access networks hinders the accurate reflection of DTs on the physical world. In this article, we present an intelligent dual time-scale network slicing strategy utilizing the long-term and short-term trends of network, aiming to make fuller use of network resources and improve the synchronization information accuracy of DTs. Specifically, within the dual time scale slicing framework, this strategy collaboratively optimize slice scaling and sensory information synchronization for DTs, aiming to maximize sensory information satisfaction and minimize the cost of slice reconfiguration and synchronization. First, at large time scales, we utilize slices to provide isolation and address deployment issues for DTs with different Quality of Service (QoS) requirements. At small time scales, we aim to enhance the adaptability of estimation tasks to dynamic environments through more flexible wireless resource allocation, further improving communication performance, and establishing DTs that closely resemble physical entities. Furthermore, to solve optimization problems at different time scales, we propose a two-layer deep reinforcement learning (DRL) framework to achieve efficient network resource interactions, in which the lower-layer control algorithms utilize the prioritized experience replay (PER) mechanism to accelerate the convergence speed. Finally, simulation results validate the effectiveness of the proposed strategy.