In-field Calibration of Low-Cost Sensors through XGBoost $\&$ Aggregate Sensor Data
This addresses the need for cost-effective and accurate air quality monitoring in urban environments, though it appears incremental as it builds on existing low-cost sensor and machine learning methods.
The paper tackles the problem of drift in low-cost air quality sensors by developing an in-field calibration model using XGBoost ensemble learning to aggregate data from neighboring sensors, which improves generalization across locations.
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.