Self-Supervised Anomaly Detection of Rogue Soil Moisture Sensors
Boje Deforce Bart Baesens Jan Diels Estefanía Serral Asensio
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.