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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.15840 |
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| _version_ | 1866915350647406592 |
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| author | Yin, Kevin Gersey, Julia Zhang, Pei |
| author_facet | Yin, Kevin Gersey, Julia Zhang, Pei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15840 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | In-field Calibration of Low-Cost Sensors through XGBoost $\&$ Aggregate Sensor Data Yin, Kevin Gersey, Julia Zhang, Pei Machine Learning 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. |
| title | In-field Calibration of Low-Cost Sensors through XGBoost $\&$ Aggregate Sensor Data |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.15840 |