With the widespread deployment of assembly robots in smart manufacturing,efficiently offloading tasks and allocating resources in highly dynamic industrial environments has become a critical challenge for Mobile Edge ...With the widespread deployment of assembly robots in smart manufacturing,efficiently offloading tasks and allocating resources in highly dynamic industrial environments has become a critical challenge for Mobile Edge Computing(MEC).To address this challenge,this paper constructs a cloud-edge-end collaborative MEC system that enables assembly robots to offload complex workflow tasks via multiple paths(horizontal,vertical,and hybrid collaboration).Tomitigate uncertainties arising frommobility,the location predictionmodule is employed.This enables proactive channel-quality estimation,providing forward-looking insights for offloading decisions.Furthermore,we propose a fairness-aware joint optimization framework.Utilizing an improved Multi-Agent Deep Reinforcement Learning(MADRL)algorithm whose reward function incorporates total system cost,positional reliability,and timeout penalties,the framework aims to balance resource distribution among assembly robots while maximizing system utility.Simulation results demonstrate that the proposed framework outperforms traditional offloading strategies.By integrating predictive mobility management with fairness-aware optimization,the framework offers a robust solution for dynamic industrial MEC environments.展开更多
为解决传统通信技术支撑电力系统负荷频率控制(Load Frequency Control,LFC)时存在的时延高、可靠性不足等问题,提出5G在LFC中的三阶段应用方案。首先构建优化参数配置与移动边缘计算(Mobile Edge Computing,MEC)部署的5G-LFC通信链路,...为解决传统通信技术支撑电力系统负荷频率控制(Load Frequency Control,LFC)时存在的时延高、可靠性不足等问题,提出5G在LFC中的三阶段应用方案。首先构建优化参数配置与移动边缘计算(Mobile Edge Computing,MEC)部署的5G-LFC通信链路,其次通过调控指令分级与优先级调度提升传输精准性,最后实现多主体递进式协同控制。通过构建耦合仿真平台,验证了所提方案对高比例新能源电网LFC需求的适配性。展开更多
基金supported by the National Key R&D Program of China under Grant Nos.2024YFD2400200 and 2024YFD2400204supported in part by the Science and Technology Development Program for the Two Zones under Grant No.2023LQ02004.
文摘With the widespread deployment of assembly robots in smart manufacturing,efficiently offloading tasks and allocating resources in highly dynamic industrial environments has become a critical challenge for Mobile Edge Computing(MEC).To address this challenge,this paper constructs a cloud-edge-end collaborative MEC system that enables assembly robots to offload complex workflow tasks via multiple paths(horizontal,vertical,and hybrid collaboration).Tomitigate uncertainties arising frommobility,the location predictionmodule is employed.This enables proactive channel-quality estimation,providing forward-looking insights for offloading decisions.Furthermore,we propose a fairness-aware joint optimization framework.Utilizing an improved Multi-Agent Deep Reinforcement Learning(MADRL)algorithm whose reward function incorporates total system cost,positional reliability,and timeout penalties,the framework aims to balance resource distribution among assembly robots while maximizing system utility.Simulation results demonstrate that the proposed framework outperforms traditional offloading strategies.By integrating predictive mobility management with fairness-aware optimization,the framework offers a robust solution for dynamic industrial MEC environments.
文摘为解决传统通信技术支撑电力系统负荷频率控制(Load Frequency Control,LFC)时存在的时延高、可靠性不足等问题,提出5G在LFC中的三阶段应用方案。首先构建优化参数配置与移动边缘计算(Mobile Edge Computing,MEC)部署的5G-LFC通信链路,其次通过调控指令分级与优先级调度提升传输精准性,最后实现多主体递进式协同控制。通过构建耦合仿真平台,验证了所提方案对高比例新能源电网LFC需求的适配性。
