Trend-adaptive multi-scale PCA for data fault detection in IoT networks
Thien-Binh Dang, Manh-Hung Tran, Duc-Tai Le, Vyacheslav V. Zalyubovskiy, Hyohoon Ahn, Hyunseung Choo · 2018
A wide range of IoT applications use information collected from networks of sensors for monitoring and controlling purposes. In such applications, the frequent appearance of fault data makes it difficult to extract correct information, thereby making confuses in interpreting and analyzing collected data. To address this problem, it is necessary to have a mechanism to detect fault data. In this paper, we present a Trend-adaptive Multi-Scale Principal Component Analysis (Trend-adaptive MS-PCA) model for data fault detection. The proposed model inherits advantages of Discrete Wavelet Transform (DWT) in capturing time-frequency information and advantages of PCA in extracting correlation among sensors' data. Experimental results on a real dataset show the high effectiveness of the proposed model in data fault detection. Moreover, the Trend-adaptive MS-PCA shows that it outperforms fault detection techniques using PCA and MS-PCA in term of fault sensitiveness.