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 data, and identifying them is a challenging task. In this paper, we propose a spatiotemporal correlation recurrent autoencoder anomaly detection (STC-RAAD) architecture that, unlike other existing architectures, in addition to the temporal correlation present in the sensor data, also involves information from neighboring sensors in the monitoring network for better reconstruction. The performance of STC-RAAD is compared with other methods on two different real data sets, for two categories of anomalies, outperforming other existing approaches, with an average increase of 38% for the detection rate and 36% for the precision.