Sensor Abnormal Detection and Recovery Using Machine Learning for IoT Sensing Systems

Feng Ke Tsai, Chien Chih Chen, Tien-Fu Chen, Tay Jyi Lin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

In sensing systems in various environments, such as environmental monitoring and smart power grid systems, sensors are usually unreliable due to improper calibration, low battery levels or hardware failures of the devices. Unreliability may cause users to make erroneous decisions or inaccurate analysis. In this paper, we propose a detect system architecture to avoid the abnormality among the sensors based on machine learning. The detection mechanism has to be in real-time by exploring the correlation among the sensors, and predicting the supplemental values via other correlated sensors. We analyze the fault data pattern in order to classify the fault type of faulty sensors and also to recover the faulty sensors for improving the reliability of sensing systems.

Original languageEnglish
Title of host publication2019 IEEE 6th International Conference on Industrial Engineering and Applications, ICIEA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages501-505
Number of pages5
ISBN (Electronic)9781728108513
DOIs
StatePublished - 14 May 2019
Event6th IEEE International Conference on Industrial Engineering and Applications, ICIEA 2019 - Tokyo, Japan
Duration: 12 Apr 201915 Apr 2019

Publication series

Name2019 IEEE 6th International Conference on Industrial Engineering and Applications, ICIEA 2019

Conference

Conference6th IEEE International Conference on Industrial Engineering and Applications, ICIEA 2019
CountryJapan
CityTokyo
Period12/04/1915/04/19

Keywords

  • IoT
  • fault detection
  • sensing applications
  • sensors

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