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arXiv · 2506.15840

In-field Calibration of Low-Cost Sensors through XGBoost $\&$ Aggregate Sensor Data

Kevin Yin·Julia Gersey·Pei Zhang
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Abstract

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.

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Kevin Yin, Julia Gersey, Pei Zhang. 2025-06-18. In-field Calibration of Low-Cost Sensors through XGBoost $\&$ Aggregate Sensor Data. https://arxiv.org/abs/2506.15840

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