TinyML Models for a Low-cost Air Quality Monitoring Device

Abstract

Low-cost air quality monitoring devices can provide high-density spatiotemporal pollution data, thus offering a better opportunity to apply machine learning. Low-cost sensor nodes usually utilize microcontrollers as the main processors, and tinyML brings machine learning (ML) models to these resource-constrained devices. In this letter, we reported the development of a low-cost air quality monitoring device with embedded tinyML models. We deployed two tinyML models on a single microcontroller and performed two tasks: predicting air quality and power parameters (using model predictor) and imputing missing features (using model imputer). The proposed model predictor can estimate parameters with a coefficient of determination above 0.70, and the model imputer effectively estimates the testing data when missing rates are below 80%. By performing the post-training quantization technique, we can further reduce the model size but slightly degrade the accuracies.