The Perils of Detecting Measurement Faults in Environmental Monitoring Networks

TL;DR

Environmental monitoring networks are prone to sensor faults, but current detection methods often misclassify genuine environmental events as faults, highlighting the need for more sophisticated, context-aware fault detection techniques.

Contribution

This paper demonstrates the limitations of existing fault detection methods in environmental networks and emphasizes the importance of incorporating event knowledge into fault detection algorithms.

Findings

Up to 45% of event measurements misclassified as faults.

Tuning algorithms to reduce false positives causes missed faults.

Current methods lack context-awareness, leading to misclassification.

Abstract

Scientists deploy environmental monitoring net-works to discover previously unobservable phenomena and quantify subtle spatial and temporal differences in the physical quantities they measure. Our experience, shared by others, has shown that measurements gathered by such networks are perturbed by sensor faults. In response, multiple fault detection techniques have been proposed in the literature. However, in this paper we argue that these techniques may misclassify events (e.g. rain events for soil moisture measurements) as faults, potentially discarding the most interesting measurements. We support this argument by applying two commonly used fault detection techniques on data collected from a soil monitoring network. Our results show that in this case, up to 45% of the event measurements are misclassified as faults. Furthermore, tuning the fault detection algorithms to avoid event misclassification, causes them to miss the majority of actual faults. In addition to exposing the tension between fault and event detection, our findings motivate the need to develop novel fault detection mechanisms which incorporate knowledge of the underlying events and are customized to the sensing modality they monitor.