用邻近传感器数据和XGBoost校准低成本空气质量传感器。
In-field Calibration of Low-Cost Sensors through XGBoost $\&$ Aggregate Sensor Data
- 用邻近传感器数据+XGBoost集成学习做现场校准。
- 降低对单个传感器精度的依赖，提升跨区域泛化能力。
- 适合城市空气监测网络部署，尤其在资源有限时。
大规模空气质量监测需依赖分布式传感，因颗粒物（PM）在城市环境中无处不在且危害严重。然而，高精度传感器成本高昂，限制了其空间部署与覆盖范围。因此，低成本传感器逐渐普及，但易受环境敏感性与制造差异影响而产生漂移。本文提出一种基于XGBoost集成学习的现场校准模型，整合邻近传感器数据，减少对单个传感器预设精度的依赖，并提升在不同地点的泛化性能。
原文摘要 · Abstract (English)
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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