To address the low efficiency of task collaboration computation caused by limited resources of Industrial Internet of Things (IIoT) devices and dynamic changes of edge server resources, a Digital Twin (DT)-assisted task offloading algorithm is proposed for ⅡoT. First, the cloud-edge-end three-layer digital twin-assisted task offloading framework is constructed by the algorithm, and the approximate optimal task offloading strategy is generated in the created digital twin layer. Second, under the constraints of task computation time and energy, the joint optimization problem of user association and task partition in the computation offloading process is studied from the perspective of delay. An optimization model is established with the goal of minimizing the task offloading time and service failure penalty. Finally, a user association and task partition algorithm based on Deep Multi-Agent Parameterized Q-Network (DMAPQN) is proposed. The approximate optimal user association and task partition strategy is obtained by each intelligent agent through continuous exploration and learning, and it is issued to the physical entity network for execution. Simulation results show that the proposed task offloading algorithm effectively reduces the task collaboration computation time and provides approximate optimal offloading strategies for each computational task.
关键词
工业物联网/数字孪生/边缘关联/任务划分/深度强化学习
Key words
Industrial Internet of Things (IIoT)/Digital Twins (DT)/Edge association/Division of tasks/Deep reinforcement learning
维普
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