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MobiAir: Unleashing Sensor Mobility for City-scale and Fine-grained Air-Quality Monitoring with AirBERT

PROCEEDINGS OF THE 2024 THE 22ND ANNUAL INTERNATIONAL CONFERENCE ON MOBILE SYSTEMS, APPLICATIONS AND SERVICES, MOBISYS 2024(2024)

Tsinghua Univ

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Abstract
Mobile air pollution sensing methods are developed to collect air quality data with higher spatial-temporal resolutions. However, existing methods cannot process the spatially mixed gas samples effectively due to the highly dynamic temporal and spatial fluctuations experienced by the sensor, leading to significant measurement deviations. We find an opportunity to tackle the problem by exploring the potential patterns from sensor measurements. In light of this, we propose MobiAir , a novel city-scale fine-grained air quality estimation system to deliver accurate mobile air quality data. First, we design AirBERT , a representation learning model to discern mixed gas concentrations. Second, we design a knowledge-informed training strategy leveraging massive unlabeled city-scale data to enhance the AirBERT performance. To ensure the practicality of MobiAir, we have invested significant efforts in implementing the software stack on our meticulously crafted Sensing Front-end , which has successfully gathered air quality data at a city-scale for more than 1200 hours. Experiments conducted on collected data show that MobiAir reduces sensing errors by 96.7% with only 44.9 ms latency, outperforming the SOTA baseline by 39.5%.
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AQI Monitoring,Mobile Crowd-Sensing,Self-supervised Learning
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要点】:MobiAir系统利用AirBERT模型,通过移动传感器在城市尺度上进行精细化的空气质量监测,创新性地通过挖掘传感器测量数据中的潜在模式来处理空间混合气体样本,显著提高了监测精度。

方法】:提出了一种基于表示学习模型AirBERT的移动空气污染感测方法,并采用了一种知识引导的训练策略,利用大量的未标记城市规模数据来增强AirBERT的性能。

实验】:在精心设计的Sensing Front-end硬件上,通过超过1200小时的实地监测收集了城市尺度的空气质量数据。实验结果表明,MobiAir系统能将感测误差降低96.7%,延迟仅为44.9毫秒,相比现有最佳基准性能提高了39.5%。