On the Prevalence of Sensor Faults in Real-World Deployments

Various sensor network measurement studies have reported instances of transient faults in sensor readings. In this work, we seek to answer a simple question: How often are such faults observed in real deployments? To do this, we first explore and characterize three qualitatively different classes of fault detection methods. Rule-based methods leverage domain knowledge to develop heuristic rules for detecting and identifying faults. Estimation methods predict "normal" sensor behavior by leveraging sensor correlations, flagging anomalous sensor readings as faults. Finally, learning-based methods are trained to statistically identify classes of faults. We find that these three classes of methods sit at different points on the accuracy/robustness spectrum. Rule-based methods can be highly accurate, but their accuracy depends critically on the choice of parameters. Learning methods can be cumbersome, but can accurately detect and classify faults. Estimation methods are accurate, but cannot classify faults. We apply these techniques to four real-world sensor data sets and find that the prevalence of faults as well as their type varies with data sets. All three methods are qualitatively consistent in identifying sensor faults in real world data sets, lending credence to our observations. Our work is a first-step towards automated on-line fault detection and classification.