@inproceedings{b90e1a1ba8c644d88f22f0f051f6d7b1,
title = "Sensor Abnormal Detection and Recovery Using Machine Learning for IoT Sensing Systems",
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.",
keywords = "IoT, fault detection, sensing applications, sensors",
author = "Tsai, {Feng Ke} and Chen, {Chien Chih} and Tien-Fu Chen and Lin, {Tay Jyi}",
year = "2019",
month = apr,
day = "12",
doi = "10.1109/IEA.2019.8715215",
language = "English",
series = "2019 IEEE 6th International Conference on Industrial Engineering and Applications, ICIEA 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "501--505",
booktitle = "2019 IEEE 6th International Conference on Industrial Engineering and Applications, ICIEA 2019",
address = "United States",
note = "6th IEEE International Conference on Industrial Engineering and Applications, ICIEA 2019 ; Conference date: 12-04-2019 Through 15-04-2019",
}