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Edge Offloading Strategy for Mobile Devices in Intelligent Factories Based on Energy Consumption and Delay
XIAO Ye 1,ZHOU Xiaoming 2,LI Zhao 2,ZHAO Chenglin 2,XU Fangmin 1 *
1.School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876;State Grid Liaoning Electric Power co., Ltd, Liaoning 110004;State Grid Liaoning Electric Power co., Ltd, Liaoning 110004;School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876;School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876
2.
*Correspondence author
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Funding: none
Opened online: 7 April 2021
Accepted by: none
Citation: XIAO Ye,ZHOU Xiaoming,LI Zhao.Edge Offloading Strategy for Mobile Devices in Intelligent Factories Based on Energy Consumption and Delay[OL]. [ 7 April 2021] http://en.paper.edu.cn/en_releasepaper/content/4754387
 
 
To cope with the challenge of successful edge offloading brought by the mobility of mobile devices in intelligent factories, this paper studies the optimization problem of the edge offloading strategy of mobile devices based on mobility. Considering the decision task flow executed by priority, the unique offloading method of a single task, the communication range of the edge server, and the delay constraint of the offloading of a single task, appropriate computing resources are selected according to the real-time location of the mobile device to offload the computing task. Based on the edge computing architecture of an intelligent factory, this paper puts forward five different computation offloading methods. From a global perspective, the energy consumption and delay of tasks offloading in local, edge, cloud center, local-edge collaboration, and local-edge-cloud collaboration are considered. In this paper, the algorithm based on genetic algorithm and particle swarm optimization is used to design the optimal decision task flow offloading strategy which can obtain the minimum energy consumption and delay. Simulation results show that the proposed algorithm can reduce the computation offloading energy consumption and delay of mobile devices.
Keywords:edge offloading; intelligent factory; mobility management; energy comsumption; delay
 
 
 

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