Department of Computer Science, National Chiao Tung University, Hsinchu 300, Taiwan.
College of Artificial Intelligence, National Chiao Tung University, Tainan 711, Taiwan.
Sensors (Basel). 2019 Nov 4;19(21):4788. doi: 10.3390/s19214788.
In an Internet of Things (IoT) system, it is essential that the data measured from the sensors are accurate so that the produced results are meaningful. For example, in AgriTalk, a smart farm platform for soil cultivation with a large number of sensors, the produced sensor data are used in several Artificial Intelligence (AI) models to provide precise farming for soil microbiome and fertility, disease regulation, irrigation regulation, and pest regulation. It is important that the sensor data are correctly used in AI modeling. Unfortunately, no sensor is perfect. Even for the sensors manufactured from the same factory, they may yield different readings. This paper proposes a solution called SensorTalk to automatically detect potential sensor failures and calibrate the aging sensors semi-automatically. Numerical examples are given to show the calibration tables for temperature and humidity sensors. When the sensors control the actuators, the SensorTalk solution can also detect whether a failure occurs within a detection delay. Both analytic and simulation models are proposed to appropriately select the detection delay so that, when a potential failure occurs, it is detected reasonably early without incurring too many false alarms. Specifically, our selection can limit the false detection probability to be less than 0.7%.
在物联网 (IoT) 系统中，从传感器测量的数据必须准确，以便产生有意义的结果。例如，在 AgriTalk 中，这是一个具有大量传感器的智能农场平台，产生的传感器数据用于几个人工智能 (AI) 模型中，为土壤微生物组和肥力、疾病调节、灌溉调节和虫害调节提供精确的农业服务。重要的是，传感器数据在 AI 建模中得到正确使用。不幸的是，没有传感器是完美的。即使是由同一家工厂制造的传感器，它们也可能产生不同的读数。本文提出了一种名为 SensorTalk 的解决方案，可自动检测潜在的传感器故障，并半自动校准老化的传感器。给出了数值示例来说明温度和湿度传感器的校准表。当传感器控制执行器时，SensorTalk 解决方案还可以检测是否在检测延迟内发生故障。提出了分析和仿真模型来适当选择检测延迟，以便在发生潜在故障时，能够合理地提前检测到，而不会产生太多误报。具体来说，我们的选择可以将误检概率限制在 0.7%以下。
