Toward Industrial Metaverse: A High Fidelity Digital Twin for IIoT With Optimized Age-Accuracy Tradeoff

Digital twins technology is a key pillar to realizing the vision of Industrial Metaverse and enabling Industry 5.0 where industries become more sustainable, resilient, and smart, while being human-centric. In this paper we aim at developing a high fidelity digital twin model consistent with its physical system for better informed decisions in terms of failure avoidance or performance enhancements. To enable this, we harness the capabilities of an unmanned aerial vehicle (UAV) to collect measurement data from Industrial Internet of Things devices that monitor various independent physical entities in a given industrial plant. Each device sends its readings to the UAV that in turn performs computations to determine the status of the physical entities. The UAV relays the resulting status details to the digital twin running at the edge of the network in a timely manner. For a high fidelity digital twin, the processed status has to be kept as close as possible to the actual status of the physical entities through ensuring high accuracy and synchronization. To do so, we introduce the age of digital twin metric based on the age of information concept to represent the degree of synchronization between the digital and physical twins then formulate the problem as a mixed integer non-convex program. Due to its complexity, we decompose the problem into two subproblems, convexify their constraints and optimize the UAV trajectory, device scheduling, wireless and computing resource allocation, and data collection. We run extensive simulations for various system parameters and demonstrate the resulting accuracy of the digital twin model while ensuring high synchronization. Simulation results corroborate the superiority of our proposed solution as compared to different baseline approaches.