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

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

研究成果: Conference contribution同行評審

15 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題2019 IEEE 6th International Conference on Industrial Engineering and Applications, ICIEA 2019
發行者Institute of Electrical and Electronics Engineers Inc.
頁面501-505
頁數5
ISBN(電子)9781728108513
DOIs
出版狀態Published - 12 4月 2019
事件6th IEEE International Conference on Industrial Engineering and Applications, ICIEA 2019 - Tokyo, Japan
持續時間: 12 4月 201915 4月 2019

出版系列

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

Conference

Conference6th IEEE International Conference on Industrial Engineering and Applications, ICIEA 2019
國家/地區Japan
城市Tokyo
期間12/04/1915/04/19

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