期刊
IEEE SENSORS JOURNAL
卷 21, 期 20, 页码 22833-22845出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2021.3105442
关键词
Sensors; Calibration; Artificial neural networks; Mice; Task analysis; Sensor phenomena and characterization; Measurement uncertainty; Calibration; imputation; Internet of Things (IoT); missing data; neural network; regression; sensors; variational autoencoder; XGBoost
资金
- Schlumberger Foundation through the Faculty for the Future Program
- Tertiary Education Trust Fund (TETFUND-Nigeria)
- SmartBOG Project through the Environmental Protection Agency (EPA) Research Program [2014-202042617/03]
The study focuses on addressing missing data in IoT sensors and proposes effective imputation strategies to enhance calibration performance. Experimental results demonstrate that VAE technique outperforms other methods in imputing missing values and improving sensor calibration performance.
IoT sensors are becoming increasingly important supplement to traditional monitoring systems, particularly for in-situ based monitoring. Data collected using IoT sensors are often plagued with missing values occurring as a result of sensor faults, network failures, drifts and other operational issues. Missing data can have substantial impact on in-field sensor calibration methods. The goal of this research is to achieve effective calibration of sensors in the context of such missing data. To this end, two objectives are presented in this paper. 1) Identify and examine effective imputation strategy for missing data in IoT sensors. 2) Determine sensor calibration performance using calibration techniques on data set with imputed values. Specifically, this paper examines the performance of Variational Autoencoder (VAE), Neural Network with Random Weights (NNRW), Multiple Imputation by Chain Equations (MICE), Random Forest-based Imputation (missForest) and K-Nearest Neighbour (KNN) for imputation of missing values on IoT sensors. Furthermore, the performance of sensor calibration via different supervised algorithms trained on the imputed dataset were evaluated. The analysis showed VAE technique to outperform the other methods in imputing the missing values at different proportions of missingness on two real-world datasets. Experimental results also showed improved calibration performance with imputed dataset.
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