Area-Adaptive Air Quality Monitoring Based on FPGA with Edge AI and Hyperspectral Imaging

This work proposes an area-adaptive system design incorporating edge AI and hyperspectral imaging (HSI) to enable mobile and flexible air quality monitoring. To achieve real-time and accurate air quality monitoring in various areas such as industrial, busy traffic, and residential areas, a band selection method based on the fuzzy-rough set theory is presented. This method enables the classification of the air quality index (AQI) level by utilizing informative HSI bands closely related to air pollution. Furthermore, a band-based AQI level classification model is proposed for spectral and spatial feature learning. We further propose an area-adaptive air quality monitoring system (AAQMS) based on FPGA by integrating the band selection method and the band-based AQI level classification model. Experiments show that when all the HSI data in 11 bands are used for AQI level classification, the AAQMS design can enhance energy efficiency by 17.16x compared to the embedded GPU platform (Nvidia Jetson Nano). Through the support of area-adaptive air quality monitoring, although the classification accuracies of AQI levels are slightly reduced, compared to using all the HSI data in 11 bands for industrial, busy traffic, and residential areas, AAQMS can accelerate the AQI level classification by 1.66x, 1.54x, and 1.4x, respectively, while reducing power consumption by 30.32%, 11.61%, and 18.06%, respectively.