The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
LSTM-Autoencoder-Based Anomaly Detection for Indoor Air Quality Time-Series Data
Open accessAbstract
Anomaly detection for indoor air quality (IAQ) data has become an important area of research as the quality of air is closely related to human health and well-being. However, traditional statistics and shallow machine-learning (ML)-based approaches in anomaly detection in the IAQ area could not detect anomalies involving the observation of correlations across several data points (i.e., often referred to as long-term dependencies). We propose a hybrid deep-learning model that combines long short-term memory (LSTM) with an autoencoder (AE) for anomaly detection tasks in IAQ to address this issue. In our approach, the LSTM network is comprised of multiple LSTM cells that work with each other to learn the long-term dependencies of the data in a time-series sequence. The AE identifies the optimal threshold based on the reconstruction loss rates evaluated on every data across all time-series sequences. Our experimental results, based on the Dunedin carbon dioxide (CO2) time-series dataset obtained through a real-world deployment of the schools in New Zealand, demonstrate a very high and robust accuracy rate (99.50%) that outperforms other similar models.
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Cites
Nguyen, H. D., Tran, K. P., Thomassey, S., & Hamad, M. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102282 · Cited by 494
Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series
Yin, C., Zhang, S., Wang, J., & Xiong, N. N. (2022) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2020.2968516 · Cited by 413
Variational LSTM Enhanced Anomaly Detection for Industrial Big Data
Zhou, X., Hu, Y., Liang, W., Ma, J., & Jin, Q. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3022432 · Cited by 402
Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model
Lin, S., Clark, R., Birke, R., Schonborn, S., Trigoni, N., & Roberts, S. (2020) · ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp40776.2020.9053558 · Cited by 312
LSTM Learning With Bayesian and Gaussian Processing for Anomaly Detection in Industrial IoT
Wu, D., Jiang, Z., Xie, X., Wei, X., Yu, W., & Li, R. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2952917 · Cited by 290
The relationships between classroom air quality and children’s performance in school
Wargocki, P., Porras-Salazar, J. A., Contreras-Espinoza, S., & Bahnfleth, W. (2020) · Building and Environment · doi:10.1016/j.buildenv.2020.106749 · Cited by 263
Improving Performance of Autoencoder-Based Network Anomaly Detection on NSL-KDD Dataset
Xu, W., Jang-Jaccard, J., Singh, A., Wei, Y., & Sabrina, F. (2021) · IEEE Access · doi:10.1109/access.2021.3116612 · Cited by 239
Self-citationISSAQ: An Integrated Sensing Systems for Real-Time Indoor Air Quality Monitoring
Kim, J.-Y., Chu, C.-H., & Shin, S.-M. (2014) · IEEE Sensors Journal · doi:10.1109/jsen.2014.2359832 · Cited by 208
Outlier Detection for Multidimensional Time Series Using Deep Neural Networks
Kieu, T., Yang, B., & Jensen, C. S. (2018) · 2018 19th IEEE International Conference on Mobile Data Management (MDM) · doi:10.1109/mdm.2018.00029 · Cited by 195
Xu, C., Chen, H., Wang, J., Guo, Y., & Yuan, Y. (2018) · Building and Environment · doi:10.1016/j.buildenv.2018.10.062 · Cited by 182
Cited by
Du, X., Zhou, C., Tian, Y.-C., & Wang, K. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3384275 · Cited by 13
Metadata
- Authors
- Wei, Y., Jang-Jaccard, J., Xu, W., Sabrina, F., Camtepe, S., & Boulic, M.
- Venue
- IEEE Sensors Journal
- Published
- 2023-02-15
- Volume
- 23
- Issue
- 4
- Pages
- 3787-3800
- ISSN
- 1530-437X (ISSN-L)
- 1558-1748
- 2379-9153
- OpenAlex
- W4313644197
- Cited by
- 269
- References
- 23
- FWCI
- 23.28
- Type
- journal article
- Language
- en
- Open access
- green
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- Wei, Y., Jang-Jaccard, J., Xu, W., Sabrina, F., Camtepe, S., & Boulic, M. (2023). LSTM-Autoencoder-Based Anomaly Detection for Indoor Air Quality Time-Series Data. IEEE Sensors Journal. https://doi.org/10.1109/jsen.2022.3230361
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