Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors

Raw data collected from NO2, O3 and NO air pollution electrochemical low-cost sensors

Recently, the monitoring of air pollution by means of low-cost sensors has become a growing research field due to the study of techniques based on machine learning to improve the sensors’ data quality. For this purpose, sensors undergo a calibration process, where these are placed in-situ nearby a regulatory reference station. The data set explained in this paper contains data from two self-built low-cost air pollution nodes deployed for four months, from January 16, 2021 to May 15, 2021, at an official air quality reference station in Barcelona, Spain. The goal of the deployment was to have five electrochemical sensors at a high sampling rate of 0.5 Hz; two NO2 sensors, two O3 sensors, and one NO sensor. It should be noted that the reference stations publish air pollution data every hour, thus at a rate of 2.7×10−4 Hz. In addition, the nodes have also captured temperature and relative humidity data, which are typically used as correctors in the calibration of low-cost sensors. The availability of the sensors’ time series at this high resolution is important in order to be able to carry out analysis from the signal processing perspective, allowing the study of sensor sampling strategies, sensor signal filtering, and the calibration of low-cost sensors among others.

A Low-Power IoT Device for Measuring Water Table Levels and Soil Moisture to Ease Increased Crop Yields

The simultaneous measurement of soil water content and water table levels is of great agronomic and hydrological interest. Not only does soil moisture represent the water available for plant growth but also water table levels can affect crop productivity. Furthermore, monitoring soil saturation and water table levels is essential for an early warning of extreme rainfall situations. However, the measurement of these parameters employing commercial instruments has certain disadvantages, with a high cost of purchase and maintenance. In addition, the handling of commercial devices makes it difficult to adapt them to the specific requirements of farmers or decision-makers. Open-source IoT hardware platforms are emerging as an attractive alternative to developing flexible and low-cost devices. This paper describes the design of a datalogger device based on open-source hardware platforms to register water table levels and soil moisture data for agronomic applications. The paper begins by describing energy-saving and wireless transmission techniques. Then, it summarizes the linear calibration of the phreatimeter sensor obtained with laboratory and field data. Finally, it shows how non-linear machine-learning techniques improve predictions over classical tools for the moisture sensor (SKU: SEN0193).