TinyML with Meta-Learning on Microcontrollers for Air Pollution Prediction

Abstract

Tiny machine learning (tinyML) involves the application of ML algorithms on resource-constrained devices such as microcontrollers. It is possible to improve tinyML performance by using a meta-learning approach. In this work, we proposed lightweight base models running on a microcontroller to predict air pollution and show how performance can be improved using a stacking ensemble meta-learning method. We used an air quality dataset for London. Deployed on a Raspberry Pi Pico microcontroller, the tinyML file sizes were 3012 bytes and 5076 bytes for the two base models we proposed. The stacked model could achieve RMSE improvements of up to 4.9% and 14.28% when predicting NO2 and PM2.5, respectively.

Keywords

• tinyML
• meta-learning
• microcontrollers
• air pollutant prediction