AQMon: A Fine-grained Air Quality Monitoring System Based on UAV Images for Smart Cities

• Published in: ACM transactions on sensor networks Vol. 20; no. 2; pp. 1 - 20
• Main Authors: Xia, Shuangqing, Xing, Tianzhang, Wu, Chase Q., Liu, Guoqing, Yang, Jiadi, Li, Kang
• Format: Journal Article
• Language: English
• Published: New York, NY ACM 19.01.2024
• Subjects: Applied computing; Computing / technology policy; Computing in government; Computing methodologies; Inductive inference; Logical and relational learning; Machine learning theory; Social and professional topics; Systems theory; Theory of computation; Smart air detection system; mobile devices; computer vision
• ISSN: 1550-4859; 1550-4867
• DOI: 10.1145/3638766

Air quality monitoring is important to the green development of smart cities. Several technical challenges exist for intelligent, high-precision monitoring, such as computing overhead, area division, and monitoring granularity. In this article, we propose a fine-grained air quality monitoring system based on visual inspection analysis embedded in unmanned aerial vehicle (UAV), referred to as AQMon. This system employs a lightweight neural network to obtain an accurate estimate of atmospheric transmittance in visual information while reducing computation and transmission overhead. Considering that air quality is affected by multiple factors, we design a dynamic fitting approach to model the relationship between scattering coefficients and PM2.5 concentration in real time. The proposed system is evaluated using public datasets and the results show that AQMon outperforms four existing methods with a processing time of 13.8 ms.