Energy-Efficient Task Offloading Based on Differential Evolution in Edge Computing System With Energy Harvesting

被引：36
作者：
Sun, Yingying [1 ,5 ]
Song, Chunhe [2 ,3 ,4 ,5 ]
Yu, Shimao [2 ,3 ,4 ,5 ]
Liu, Yiyang [2 ,3 ,4 ,5 ]
Pan, Hao [1 ]
Zeng, Peng [2 ,3 ,4 ,5 ]
机构：
[1] Shenyang Univ Chem Technol, Coll Informat Engn, Shenyang 110142, Peoples R China
[2] Chinese Acad Sci, Shenyang Inst Automat, State Key Lab Robot, Shenyang 110016, Peoples R China
[3] Chinese Acad Sci, Key Lab Networked Control Syst, Shenyang 110016, Peoples R China
[4] Chinese Acad Sci, Inst Robot & Intelligent Mfg, Shenyang 110169, Peoples R China
[5] Chinese Acad Sci, Shenyang Inst Automat, Shenyang 110016, Peoples R China
来源：
IEEE ACCESS | 2021年 / 9卷
基金：
中国国家自然科学基金;
关键词：
Edge computing; task offloading; energy harvesting; differential evolutionary algorithm;
D O I：
10.1109/ACCESS.2021.3052901
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：16383 / 16391
页数：9
相关论文
共 22 条
[1]  
[白云飞 Bai Yunfei], 2020, [机器人, Robot], V42, P301
[2]   Energy-Spectrum Efficiency Trade-Off in Energy Harvesting Cooperative Cognitive Radio Networks [J].
Chatterjee, Suhhankar ;
Maity, Santi P. ;
Acharya, Tamaghna .
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING, 2019, 5 (02) :295-303
[3]  
Chen X., 2018, P IEEE 88 VEH TECHN, P1, DOI [10.1109/VTCFall.2018.8691033, DOI 10.1109/VTCFALL.2018.8691033]
[4]   Shannon meets Tesla: Wireless information and power transfer [J].
Grover, Pulkit ;
Sahai, Anant .
2010 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY, 2010, :2363-2367
[5]   Dynamic Offloading for Energy Harvesting Mobile Edge Computing: Architecture, Case Studies, and Future Directions [J].
Li, Bin ;
Fei, Zesong ;
Shen, Jian ;
Jiang, Xiao ;
Zhong, Xiaoxiong .
IEEE ACCESS, 2019, 7 :79877-79886
[6]   Transmission with Energy Harvesting Nodes in Fading Wireless Channels: Optimal Policies [J].
Ozel, Omur ;
Tutuncuoglu, Kaya ;
Yang, Jing ;
Ulukus, Sennur ;
Yener, Aylin .
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2011, 29 (08) :1732-1743
[7]   A Survey of Energy and Spectrum Harvesting Technologies and Protocols for Next Generation Wireless Networks [J].
Padhy, Ashish ;
Joshi, Sandeep ;
Bitragunta, Sainath ;
Chamola, Vinay ;
Sikdar, Biplab .
IEEE ACCESS, 2021, 9 :1737-1769
[8]   A Cloud Edge Collaborative Intelligence Method of Insulator String Defect Detection for Power IIoT [J].
Song, Chunhe ;
Xu, Wenxiang ;
Han, Guangjie ;
Zeng, Peng ;
Wang, Zhongfeng ;
Yu, Shimao .
IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (09) :7510-7520
[9]  
[王楚晴 Wang Chuqing], 2020, [信息与控制, Information and Control], V49, P714
[10]   Optimal Multi-User Computation Offloading Strategy for Wireless Powered Sensor Networks [J].
Wang, Luhan ;
Shao, Hua ;
Li, Jingjing ;
Wen, Xiangming ;
Lu, Zhaoming .
IEEE ACCESS, 2020, 8 :55150-55160
123