Communication and Computation Resource Allocation for Digital Twin in IIoT Systems

Abstract

In order to construct the digital twin (DT) of the manufacturing process in industrial IoT (IIoT) systems, multiple sensors and IIoT equipments (IIEs) are deployed for process monitoring (PM) and other tasks. Heterogeneous data generated from different tasks are transmitted through the shared wireless network to edge computing devices (ECDs) for further decisions. However, either communication or computation resources are limited in industrial environment, which should be allocated efficiently. In this paper, in order to improve the overall efficiency for DT construction, we adjust transmission power, allocate band- width and determine computing resource allocation for various tasks, which are jointly optimized by formulating a mixed integer nonlinear programming (MINLP) problem. We decompose the original problem into two subproblems and propose a value-of- information (VoI) based algorithm for PM tasks. Then, we characterize the relationship between state estimation convergence for PM and the maximum allowed offloading aomunt for other computation tasks at each time slot. Based on that relationship, we determine the transmission power and the computing resource allocation policy for IIEs. Finally, simulation results prove that our method can improve the overall efficiency while guaranteeing state estimation convergence requirements for PM systems and stability of computation task queues for other tasks.