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工业物联网中数字孪生辅助任务卸载算法

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针对工业物联网(IIoT)设备资源有限和边缘服务器资源动态变化导致的任务协同计算效率低等问题,该文提出一种工业物联网中数字孪生(DT)辅助任务卸载算法.首先,该算法构建了云-边-端3层数字孪生辅助任务卸载框架,在所创建的数字孪生层中生成近似最佳的任务卸载策略.其次,在任务计算时间和能量的约束下,从时延的角度研究了计算卸载过程中用户关联和任务划分的联合优化问题,建立了最小化任务卸载时间和服务失败惩罚的优化模型.最后,提出一种基于深度多智能体参数化Q网络(DMAPQN)的用户关联和任务划分算法,通过每个智能体不断地探索和学习,以获取近似最佳的用户关联和任务划分策略,并将该策略下发至物理实体网络中执行.仿真结果表明,所提任务卸载算法有效降低了任务协同计算时间,同时为每个计算任务提供近似最佳的卸载策略.
Digital Twin-assisted Task Offloading Algorithms for the Industrial Internet of Things
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.

Industrial Internet of Things (IIoT)Digital Twins (DT)Edge associationDivision of tasksDeep reinforcement learning

唐伦、单贞贞、文明艳、李荔、陈前斌

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重庆邮电大学通信与信息工程学院 重庆 400065

移动通信技术重庆市重点实验室 重庆 400065

工业物联网 数字孪生 边缘关联 任务划分 深度强化学习

国家自然科学基金重庆市教委科学技术研究计划川渝联合实施重点研发项目贵州省教育厅自然科学研究项目

62071078KJZD-M2018006012021YFQ0053黔教合KY字[2021]236

2024

10.11999/JEIT230317

电子与信息学报
中国科学院电子学研究所 国家自然科学基金委员会信息科学部

电子与信息学报

CSTPCD北大核心
影响因子：1.302
ISSN：1009-5896
年,卷(期)：2024.46(4)