Self-Supervised Anomaly Detection of Rogue Soil Moisture Sensors
This addresses the challenge of ensuring data quality in agricultural IoT systems, where unlabeled and voluminous data can lead to incorrect analytics, though it is incremental as it builds on existing self-supervised and contrastive learning techniques.
The paper tackles the problem of detecting rogue soil moisture sensors in IoT agriculture data without prior labels, using a self-supervised method with a neural network and contrastive loss enhanced by Dynamic Time Warping for negative sampling, achieving promising results on a dataset from pear orchards.
IoT data is a central element in the successful digital transformation of agriculture. However, IoT data comes with its own set of challenges. E.g., the risk of data contamination due to rogue sensors. A sensor is considered rogue when it provides incorrect measurements over time. To ensure correct analytical results, an essential preprocessing step when working with IoT data is the detection of such rogue sensors. Existing methods assume that well-behaving sensors are known or that a large majority of the sensors is well-behaving. However, real-world data is often completely unlabeled and voluminous, calling for self-supervised methods that can detect rogue sensors without prior information. We present a self-supervised anomalous sensor detector based on a neural network with a contrastive loss, followed by DBSCAN. A core contribution of our paper is the use of Dynamic Time Warping in the negative sampling for the triplet loss. This novelty makes the use of triplet networks feasible for anomalous sensor detection. Our method shows promising results on a challenging dataset of soil moisture sensors deployed in multiple pear orchards.