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

TL;DR

This paper introduces an in-field calibration method for low-cost air quality sensors using XGBoost ensemble learning to improve accuracy and coverage in urban monitoring.

Contribution

It proposes a novel calibration model that leverages aggregate sensor data and machine learning to enhance low-cost sensor reliability in real-world settings.

Findings

Improved sensor accuracy through ensemble calibration.

Reduced dependence on individual sensor precision.

Enhanced spatial coverage of air quality monitoring.

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.