To Transmit or Predict: An Efficient Industrial Data Transmission Scheme With Deep Learning and Cloud-Edge Collaboration
Yu Wu, Bo Yang, Dafeng Zhu, Qi Liu, Cheng Li, Cailian Chen, Xinping Guan
Abstract
Many computation-intensive industrial applications need to be run in the cloud, which relies on a lot of sharply varying data transmitted from the industrial field. To save the communication bandwidth and ensure data with required accuracy obtained by the cloud, we design a data transmission architecture based on dual prediction scheme and cloud-edge collaboration and a dual-mode algorithm based on deep learning. With the proposed architecture, a deep learning model is deployed and synchronized on the edge and cloud sides. Further, the proposed algorithm can help the cloud for computation with locally predicted data or real-time data from the edge, depending on whether the predicted data are adequately accurate. A physical validation platform including a sensor, an edge gateway, and a cloud server is built, and drastically changing real vibration data are collected to validate the proposed scheme. The results show that the proposed scheme can reduce 88.66% of data transmission while guaranteeing deviations less than 0.1.