期刊
IEEE ACCESS
卷 9, 期 -, 页码 16383-16391出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3052901
关键词
Edge computing; task offloading; energy harvesting; differential evolutionary algorithm
资金
- National Key Research and Development Program of China [2018YFB1700103]
- Key Research and Development Program of Jiangsu Province [BE2020001-1]
- National Natural Science Foundation of China [U1908212, 61773368]
- Industrial Internet Innovation Development Project (Edge Computing Test Bed), Revitalizing Liaoning Outstanding Talents [XLYC1907057]
- State Grid Corporation Science and Technology Project [SG2NK00DWJS1800123]
This paper proposes an energy-efficient task offloading method optimized by differential evolution to optimize the energy efficiency of edge computing system with energy harvesting. Experimental results show that the method can effectively solve the energy shortage problem of micro-equipment and extend the service life of the equipment.
To optimize the energy efficiency of edge computing system with energy harvesting, this paper proposes an energy-efficient task offloading method optimized by differential evolution. First, a wireless edge computing network model is established to analyze the energy harvesting, task offloading and task calculation of the system, as well as the total number of calculated bits and total energy consumption of the system. Second, according to the total number of calculated bits and total energy consumption of the system, an objective function is established to optimize the energy efficiency of system, and a differential evolution based optimization method is proposed, with which the optimal energy efficiency of system calculation, offloading time, calculation time and frequency are obtained. Experimental results show that the proposed method can not only achieve better convergence effect, but also can effectively solve the energy shortage problem of the micro-equipment and extend the service life of the equipment.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据