环境监测网络中测量故障检测的风险
The Perils of Detecting Measurement Faults in Environmental Monitoring Networks
摘要：
科学家部署环境监测网络，以发现以前无法观察到的现象，并量化他们测量的物理量的细微空间和时间差异。我们的经验，与其他人分享，已经表明，这种网络收集的测量值受到传感器故障的干扰。作为响应，在文献中已经提出了多个故障检测技术。然而，在本文中，我们认为，这些技术可能会错误分类的事件（如降雨事件土壤水分测量）的故障，可能会丢弃最有趣的测量。我们支持这一论点，采用两种常用的故障检测技术从土壤监测网络收集的数据。我们的研究结果表明，在这种情况下，高达45%的事件测量被错误分类为故障。此外，调整故障检测算法以避免事件误分类，导致它们错过大多数实际故障。除了暴露故障和事件检测之间的紧张关系，我们的研究结果激励需要开发新的故障检测机制，将知识的基础事件和定制的传感模式，他们监测。
英文摘要：
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.
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