Graph Signal Reconstruction Techniques for IoT Air Pollution Monitoring Platforms

Air pollution monitoring platforms play a very important role in preventing and mitigating the effects of pollution. Recent advances in the field of graph signal processing have made it possible to describe and analyze air pollution monitoring networks using graphs. One of the main applications is the reconstruction of the measured signal in a graph using a subset of sensors. Reconstructing the signal using information from neighboring sensors is a key technique for maintaining network data quality, with examples including filling in missing data with correlated neighboring nodes, creating virtual sensors, or correcting a drifting sensor with neighboring sensors that are more accurate. This article proposes a signal reconstruction framework for air pollution monitoring data where a graph signal reconstruction model is superimposed on a graph learned from the data. Different graph signal reconstruction methods are compared on actual air pollution data sets measuring O3, NO2, and PM10. The ability of the methods to reconstruct the signal of a pollutant is shown, as well as the computational cost of this reconstruction. The results indicate the superiority of methods based on kernel-based graph signal reconstruction, as well as the difficulties of the methods to scale in an air pollution monitoring network with a large number of low-cost sensors. However, we show that the scalability of the framework can be improved with simple methods, such as partitioning the network using a clustering algorithm.

Automated summary

Air pollution monitoring platforms are crucial in preventing and mitigating pollution. Recent advances in graph signal processing allow for the description and analysis of air pollution monitoring networks through graphs. This article proposes a signal reconstruction framework for air pollution data and compares different methods on actual datasets. The results show the superiority of kernel-based graph signal reconstruction methods, as well as the challenges in scaling with a large number of low-cost sensors in the monitoring network.