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

• URL: http://arxiv.org/abs/2506.15840v1
• Date: Wed, 18 Jun 2025 19:40:32 GMT
• Title: In-field Calibration of Low-Cost Sensors through XGBoost $\&$ Aggregate Sensor Data
• Authors: Kevin Yin, Julia Gersey, Pei Zhang,
• Abstract summary: Precision comes at a cost: highly accurate sensors are expensive, limiting the spatial deployments and thus their coverage.<n>Low-cost sensors have become popular, though they are prone to drift caused by environmental sensitivity and manufacturing variability.<n>This paper presents a model for in-field sensor calibration using XGBoost ensemble learning to consolidate data from neighboring sensors.
• Score: 2.7832348184252056
• License: http://creativecommons.org/licenses/by/4.0/
• 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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