华南理工大学学报（自然科学版）2025，Vol.53Issue(3)：88-96,9.DOI:10.12141/j.issn.1000-565X.240262
数字孪生辅助的工业物联网边缘计算任务卸载和资源分配策略
Digital Twin Assisted Edge Computing Task Offloading and Resource Allocation Strategy in Industrial Internet of Things
摘要
Abstract
In the industrial Internet of Things,the reliability of mobile edge computing largely depends on the wire-less channel conditions.In order to process the influence of imperfect channel state information to the system,this paper proposed a digital twin assisted mobile edge computing energy consumption optimization method.For the task offloading problem in industrial Internet of Things,a digital twin model of devices and channels in the edge computing system was established.Considering imperfect channel state information,the joint optimization of offloading decisions,transmission power,channel resources,and computational resources is performed with the aim of minimizing the total system energy consumption.To deal with the proposed nonlinear non convex problem of mixed integers,the probabilistic delay constraint was transformed and the original problem was decomposed into two sub-problems,and a joint optimization algorithm with the assistance of digital twins based on continuous convex approximation was proposed.Firstly,the original problem was relaxed to obtain resource allocation schemes and task offloading priorities.Then,the offloading priority of each terminal device was sorted in descending order.The complete task offloading scheme was obtained by solving the iterative optimization problem.Finally,simulation results show that,compared to other benchmark schemes,the proposed computational offloading optimization scheme significantly reduces the total energy consumption of the system.关键词
移动边缘计算/任务卸载/数字孪生网络/非完美信道状态信息Key words
mobile edge computing/task offloading/digital twin network/imperfect channel state information分类
信息技术与安全科学引用本文复制引用
李松,李一鸣,李顺..数字孪生辅助的工业物联网边缘计算任务卸载和资源分配策略[J].华南理工大学学报（自然科学版）,2025,53(3):88-96,9.基金项目
江苏省自然科学基金项目(BK20200650) （BK20200650）
西安市网络融合通信重点实验室开放基金项目(2022NCC-N103) （2022NCC-N103）
海南省省属科研院所技术创新项目(KYYSGY2024-005) （KYYSGY2024-005）
工信部项目(CBG01N23-01-04) Supported by the Natural Science Foundation of Jiangsu Province(BK20200650) （CBG01N23-01-04）
