Effective large-scale air quality monitoring necessitates distributed sensing due to the pervasive and harmful nature of particulate matter (PM), particularly in urban environments. However, precision comes at a cost: highly accurate sensors are expensive, limiting the spatial deployments and thus their coverage. As a result, low-cost sensors have become popular, though they are prone to drift caused by environmental sensitivity and manufacturing variability. This paper presents a model for in-field sensor calibration using XGBoost ensemble learning to consolidate data from neighboring sensors. This approach reduces dependence on the presumed accuracy of individual sensors and improves generalization across different locations.
@article{arxiv.2506.15840,
title = {In-field Calibration of Low-Cost Sensors through XGBoost $\&$ Aggregate Sensor Data},
author = {Kevin Yin and Julia Gersey and Pei Zhang},
journal= {arXiv preprint arXiv:2506.15840},
year = {2025}
}