空气质量低成本传感器占空比系统中的采样权衡
Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors
摘要：
近年来，使用低成本传感器和高精度仪器监测空气污染显示出可喜的成果。这些传感器面临的主要挑战之一是其数据的质量，这就是为什么主要的努力集中在使用机器学习技术来校准传感器以提高数据质量。然而，有一个方面被忽视了，那就是这些传感器安装在节点上，如果它们是电池供电的，可能会有能耗限制。在本文中，我们展示了通常的传感器数据收集过程，我们研究了现有的权衡这种传感器的采样，传感器校准的质量，以及所涉及的功耗。为此，我们在高频测量对流层臭氧、二氧化氮和一氧化氮的原型节点上进行实验。结果表明，传感器采样策略直接影响空气污染估计的质量，并且每种类型的传感器可能需要不同的采样策略。此外，当传感器具有大约两分钟的响应时间时，可以实现0.1的占空比，并且当传感器响应时间可以忽略不计时，可以实现0.01和0.02之间的占空比，用每小时的参考值进行校准，并且保持与节点连接到不间断电源时类似的校准数据的质量。
英文摘要：
The use of low-cost sensors in conjunction with high-precision instrumentation for air pollution monitoring has shown promising results in recent years. One of the main challenges for these sensors has been the quality of their data, which is why the main efforts have focused on calibrating the sensors using machine learning techniques to improve the data quality. However, there is one aspect that has been overlooked, that is, these sensors are mounted on nodes that may have energy consumption restrictions if they are battery-powered. In this paper, we show the usual sensor data gathering process and we study the existing trade-offs between the sampling of such sensors, the quality of the sensor calibration, and the power consumption involved. To this end, we conduct experiments on prototype nodes measuring tropospheric ozone, nitrogen dioxide, and nitrogen monoxide at high frequency. The results show that the sensor sampling strategy directly affects the quality of the air pollution estimation and that each type of sensor may require different sampling strategies. In addition, duty cycles of 0.1 can be achieved when the sensors have response times in the order of two minutes, and duty cycles between 0.01 and 0.02 can be achieved when the sensor response times are negligible, calibrating with hourly reference values and maintaining a quality of calibrated data similar to when the node is connected to an uninterruptible power supply.
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