A Deep Learning Based Efficient Data Transmission for Industrial Cloud-Edge Collaboration

被引：1
作者：
Wu, Yu [1 ,2 ,3 ]
Yang, Bo [1 ,2 ,3 ]
Li, Cheng [1 ,2 ,3 ]
Liu, Qi [1 ,2 ,3 ]
Liu, Yuxiang [1 ,2 ,3 ]
Zhu, Dafeng [1 ,2 ,3 ]
机构：
[1] Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200240, Peoples R China
[2] Minist Educ China, Key Lab Syst Control & Informat Proc, Beijing, Peoples R China
[3] Shanghai Engn Res Ctr Ind Intelligent Control & M, Shanghai 200240, Peoples R China
来源：
2022 IEEE 31ST INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS (ISIE) | 2022年
关键词：
industrial internet of things (IIoT); cloud-edge collaboration; edge computing; deep learning (DL); data prediction; transmission reduction;
D O I：
10.1109/ISIE51582.2022.9831607
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The tremendous data transmission between the cloud server and edge gateways accelerates the realization of the intelligent factory. However, it consumes enormous band-width resources and leads to the problem that limited factory bandwidth can not meet the large-scale high-density online data transmission. Therefore, data transmission between the cloud server and edge gateways must be reduced IAl enable large scale cloud-edge interaction. To acltieve this purpose, we propose a deep learning (DL) based data transmission reduction (DPTR) sebeme for cloud-edge collaboration, which combines the cloud-edge characteristics to reduce the data transmission volume online while ensuring data accuracy. Meanwhile, we built a physical verification platform including sensor, edge gateway, and cloud server to collect real data and validate the DPTR sebeme. Based on the physical validation platform and real data, we esperimentally demonstrate that the proposed scheme can reduce the data transmission by 76.83% while guaranteeing the relative deviation of less than 10%, even for drastically changing vibration data.
引用
收藏
页码：1202 / 1207
页数：6
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