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.

翻译：物联网数据是农业成功数字化转型的核心要素。然而，物联网数据本身也面临一系列挑战，例如由流浪传感器引发的数据污染风险。当传感器随时间提供错误测量结果时，即被视为流浪传感器。为确保分析结果的准确性，处理物联网数据时的一个关键预处理步骤是检测此类流浪传感器。现有方法假设正常传感器已知或绝大多数传感器运行正常，但现实世界中的数据往往完全未标注且规模庞大，需要能够无需先验信息即可检测流浪传感器的自监督方法。我们提出了一种基于对比损失神经网络结合DBSCAN的自监督异常传感器检测器。本文的核心贡献在于利用动态时间规整进行三元组损失的负样本采样。这一创新使得三元组网络在异常传感器检测中的应用成为可能。该方法在部署于多个梨园的土壤水分传感器挑战性数据集上展现了令人鼓舞的结果。