Multiple Local-Edge-Cloud Collaboration Strategies in Industrial Internet of Things: A Hybrid Genetic-Based Approach
Multiple Local-Edge-Cloud Collaboration Strategies in Industrial Internet of Things: A Hybrid Genetic-Based Approach
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DOI：
10.1155/2022/1486580
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发表时间：
2022-09
影响因子：
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通讯作者：
Xiaokai Liu;Fangmin Xu;Ye Xiao;Xiaoming Zhou;Zhao Li;Cheng-lin Zhao;M. Zhang
中科院分区：
文献类型：
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作者：
Xiaokai Liu;Fangmin Xu;Ye Xiao;Xiaoming Zhou;Zhao Li;Cheng-lin Zhao;M. Zhang
为了应对智能工厂中移动设备移动性带来的成功边缘卸载的挑战，本文研究了基于移动性的移动设备边缘卸载策略的优化问题。综合考虑优先执行的决策任务流程、单个任务独特的卸载方式、边缘服务器的通信范围以及单个任务卸载的时延约束，根据移动设备的实时位置选择合适的计算资源来卸载计算任务。基于智能工厂的边缘计算架构，本文提出了五种不同的计算卸载方法。从全局角度考虑本地、边缘、云中心、本地-边缘协作、本地-边缘-云协作等任务卸载的能耗和延迟。本文采用基于遗传算法和粒子群优化的算法来设计并获得能耗和延迟最低的决策任务流卸载策略。仿真结果表明，该算法能够降低移动设备的计算卸载能耗和延迟。
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 mode 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 the genetic algorithm and particle swarm optimization is used to design and obtain the decision task flow offloading strategy with the lowest energy consumption and delay. Simulation results show that the proposed algorithm can reduce the computation offloading energy consumption and delay of mobile devices.