Graph Learning Techniques Using Structured Data for IoT Air Pollution Monitoring Platforms

Existing air pollution monitoring networks use reference stations as the main nodes. The addition of low-cost sensors calibrated in-situ with machine learning techniques allows the creation of heterogeneous air pollution monitoring networks. However, current monitoring networks or calibration techniques have limitations in estimating missing data, adding virtual sensors or recalibrating sensors. The use of graphs to represent structured data is an emerging area of research that allows the use of powerful techniques to process and analyze data for air pollution monitoring networks. In this article, we compare two techniques that rely on structured data, one based on statistical methods and the other on signal smoothness, with a baseline technique based on the distance between nodes and that does not rely on the measured signal data. To compare these techniques, the sensor signal is reconstructed with a supervised method based on linear regression and a semisupervised method based on Laplacian interpolation, which allows reconstruction even when data is missing. The results, on data sets measuring O-3, NO2, and PM10, show that the signal smoothness-based technique behaves better than the other two, and used together with the Laplacian interpolation is near optimal with respect to the linear regression method. Moreover, in the case of heterogeneous networks, the results show a reconstruction accuracy similar to the in-situ calibrated sensors. Thus, the use of the network data increases the robustness of the network against possible sensor failures.

Automated summary

The article compares two techniques based on structured data, one based on statistical methods and the other on signal smoothness, as well as a baseline technique that does not rely on measured signal data. The results show that the signal smoothness-based technique performs better than the other two on datasets measuring O-3, NO2, and PM10, and when used together with Laplacian interpolation, it is near optimal compared to linear regression. Additionally, in heterogeneous networks, the reconstruction accuracy is similar to in-situ calibrated sensors, increasing the robustness of the network against sensor failures.