Intelligent Computation Offloading Based on Digital Twin-Enabled 6G Industrial IoT

Abstract

Digital twin (DT) technology, which can provide larger and more accurate amounts of data, combined with the additional computility brought by virtual environments, can support more complex connected industrial applications. Simultaneously, the development and maturity of 6G technology has driven the development of industrial manufacturing and greatly improved the operational efficiency of the industrial internet of things (IIoT). Nevertheless, massive data, heterogeneous IoT device attributes, and the deterministic and bounded latency for delay sensitive applications are major barriers to improving the quality of services (QoS) in the IIoT. In this article, we first construct a new DT-enabled network architecture and computation offloading delay model in the IIoT. Then, the computation offloading problem is formulated with the goal of minimizing the overall task completion delay and achieving resource allocation. Since the formulation is a joint optimization problem, we use deep reinforcement learning (DRL) to solve the original problem, which can be described by a Markov decision process (MDP). Numerical results show that our proposed scheme is able to improve the task success rate and reduce the task processing end-to-end delay compared to the benchmark schemes.