arXiv：cs.LG· Jinyong Yun, Seokho Ahn, Hyungjin Kim, Sungbok Shin, Young-Duk Seo·· 3 小时前
低成本传感器室内空气质量监测校准：数据集、评估场景与轻量模型
Low-Cost Sensor Calibration for Indoor Air Quality Monitoring: A Dataset, Evaluation Scenarios, and a Lightweight Model
AI 导读
研究发布了一个为期六个月的数据集，涵盖五个地点的低成本与参考传感器多变量室内空气质量测量值及上下文元数据，用于解决传统校准需在每个部署点共置参考传感器的局限。基于该数据集定义了参考高效、位置迁移、长期漂移与事件条件四种评估场景，并提出一种结合输入窗口压缩与残差时序及特征融合的轻量时序模型，在全部场景中实现强校准性能与低边缘推理成本。
正文
Abstract:Low-cost sensors enable scalable indoor air quality monitoring but require calibration because of nonlinear distortions, noise, and temporal drift. The conventional strict pairwise calibration setting requires a co-located reference sensor at each deployment location and does not account for spatial and temporal heterogeneity. To address these limitations, we introduce a six-month dataset comprising multivariate indoor air-quality measurements from low-cost and reference sensors with contextual metadata collected at five locations. Using this dataset, we define four evaluation scenarios. The reference-efficient and location-transfer scenarios evaluate spatial generalization, whereas the long-term drift and event-conditioned scenarios assess robustness to gradual and abrupt distribution shifts. Based on these scenarios, we derive design requirements and propose a lightweight temporal model that combines input-window compression with residual temporal and feature fusion. Experiments show strong calibration performance across all four scenarios with low edge-inference cost.
| Comments: | 8 pages, 3 figures, 7 tables |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.11236 [cs.LG] |
| (or arXiv:2610.11236v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11236 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jinyong Yun [view email]
[v1]
Thu, 8 Oct 2026 04:35:35 UTC (590 KB)
来源：arXiv：cs.LG · arxiv.org