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

Abstract

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.

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

“…In this line, and as an example of use of this data set, Ferrer-Cid et al. [5] have studied the trade-offs present between the sensor sampling frequency, the quality of the resulting sensor calibration, and the energy consumption of a sensing subsystem. Basically, it is shown how with a larger sampling period a good data quality can be obtained in exchange for significant energy savings.…”

“…The temperature and relative humidity sensors give readings in C and % respectively. Data source location Institution: Universitat Politécnica de Catalunya (UPC) City/Town/Region: Barcelona Country: Spain GPS coordinates for collected samples/data: Palau Reial Reference Station, Latitude 41.3872889, Longitude 2.115666667 Data accessibility Repository name: Zenodo Files names: 20001_O3_NO2_raw.csv, 20002_O3_NO2_raw.csv, 20001_NO_raw.csv, 20001_T_RH_raw.csv, 20002_T_RH_raw.csv URL: https://doi.org/10.5281/zenodo.5770589 [4] Related research article P. Ferrer-Cid, J. Garcia-Calvete, A. Main-Nadal, Z. Ye, J. M. Barcelo-Ordinas, J. Garcia-Vidal, Sampling trade-offs in duty-cycled systems for air quality low-cost sensors, Sensors 22 (2022) https://doi.org/10.3390/s22103964 [5] . …”

Temporal Pattern-Based Denoising and Calibration for Low-Cost Sensors in IoT Monitoring Platforms

“…Finally, we define two other data sets called D. In all data sets, the samples are hourly (T =1 h) and are taken with the same aggregation strategy as the reference stations; minute samples are taken and aggregated into hourly samples [30]. In order to have daily 24-hour samples, in case of data loss, e.g.…”

State-of-the-Art Low-Cost Air Quality Sensors, Assemblies, Calibration and Evaluation for Respiration-Associated Diseases: A Systematic Review

“…Clark and Lyons utilized the strategy of the electrochemical detection of oxygen or hydrogen peroxide to measure glucose in biological samples [9]. The ubiquity of impedance is mainly leveraged to realize gas transducers [10]. Optical sensors are passive, i.e., require an external field excitation source to inject some energy into the observation specimen for measurement [11][12][13].…”