Missing Value Filling Based on the Collaboration of Cloud and Edge in Artificial Intelligence of Things

Tian Wang, Haoxiong Ke, Alireza Jolfaei, Sheng Wen, Mohammad Sayad Haghighi, Shuqiang Huang

Research output: Contribution to journalArticlepeer-review

Abstract

With the development of 5G and IoT, all kinds of real life data are collected and recorded by a large number of sensors. It is of great significance to mine and analyze the hidden information in the data to provide predictions for the future. However, due to interference or instability of the collection equipment, the collected sensory data is often incomplete, and this incompleteness, hinders the in-depth analysis of data in the cloud. Therefore, processing around missing values is particularly important. Relying on cloud machine learning methods is not enough to deal with the problem of missing data in the Artificial Intelligence of Things (AIoT) environment, and edge computing provides a promising solution. In this paper, Gated Recurrent Units Filling (GRUF) is applied to the edge nodes. A mobile edge node can not only find the historical information of the current missing data node, but also grasp the data of the nodes adjacent to the missing data node. This ensures that the missing data is restored to the maximum extent at the source. The experimental results show that the missing value filling based on edge computing (MVFEC) not only outperforms other filling methods in quality, but also greatly reduces the bandwidth and energy consumption of AIoT.

Original languageEnglish
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Early online date15 Nov 2021
DOIs
Publication statusE-pub ahead of print - 15 Nov 2021
Externally publishedYes

Keywords

  • Artificial Intelligence of Things
  • Cloud computing
  • Edge computing
  • Energy consumption
  • Filling
  • Internet of Things
  • Missing Value Filling
  • Recurrent Neural Networks
  • Sensors
  • Time series analysis
  • Wireless sensor networks

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