发表机构
Inha University; Sogang University（仁荷大学; 西江大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对低成本室内空气质量传感器校准的局限，构建含五地点六个月数据的数据集并定义四种评估场景，提出结合输入窗口压缩与残差融合的轻量级时间模型，实现低边缘推理成本下的优异校准性能。
AI 中文摘要
低成本传感器可实现规模化室内空气质量监测，但因存在非线性失真、噪声和时间漂移需进行校准。传统严格成对校准设置要求每个部署位置配备共址参考传感器，且未考虑空间和时间异质性。为解决这些局限，我们引入了一个为期六个月的数据集，包含来自低成本传感器和参考传感器的多变量室内空气质量测量数据，以及在五个地点收集的上下文元数据。利用该数据集，我们定义了四种评估场景：参考高效场景和位置迁移场景评估空间泛化能力，长期漂移场景和事件条件场景评估对渐进式和突发分布变化的鲁棒性。基于这些场景，我们推导了设计要求，并提出了一种轻量级时间模型，该模型结合了输入窗口压缩与残差时间及特征融合。实验表明，该模型在所有四种场景下均展现出优异的校准性能，且边缘推理成本较低。
英文摘要
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.
Comments8 pages, 3 figures, 7 tables
