Leveraging spatiotemporal correlations with recurrent autoencoders for sensor anomaly detection
The introduction of high- and low-cost Internet of Things (IoT) sensors in air quality monitoring networks, in addition to providing a cost-effective solution for monitoring pollutant levels, also brings with it the challenge of ensuring data reliability. These sensors can present anomalies in the d...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/417733 |
| Acceso en línea: | https://hdl.handle.net/2117/417733 https://dx.doi.org/10.1109/JIOT.2024.3416525 |
| Access Level: | acceso abierto |
| Palabra clave: | Air quality Anomaly detection Autoencoder Internet of Things (IoT) Low-cost sensors (LCSs) Monitoring stations Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors |
| Sumario: | The introduction of high- and low-cost Internet of Things (IoT) sensors in air quality monitoring networks, in addition to providing a cost-effective solution for monitoring pollutant levels, also brings with it the challenge of ensuring data reliability. These sensors can present anomalies in the data and identifying them is a challenging task. In this article, we propose a spatiotemporal correlation recurrent autoencoder anomaly detection (STC-RAAD) architecture that unlike the other existing architectures, in addition to the temporal correlation present in the sensor data, also involves information from the neighboring sensors in the monitoring network for better reconstruction. The performance of STC-RAAD is compared with the other methods on two different real data sets, for two categories of anomalies, outperforming the other existing approaches with an average increase of 38% for the detection rate and 36% for the precision. |
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