UAV Aided Aerial-Ground IoT for Air Quality Sensing in Smart City: Architecture, Technologies, and Implementation

• Published in: IEEE network Vol. 33; no. 2; pp. 14 - 22
• Main Authors: Hu, Zhiwen, Bai, Zixuan, Yang, Yuzhe, Zheng, Zijie, Bian, Kaigui, Song, Lingyang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Air monitoring; Air pollution; Air quality; Architecture; Data processing; Detection; Energy management; Environmental monitoring; Internet of Things; Outdoor air quality; Pollution monitoring; Power consumption; Power control; Power demand; Public health; Real-time systems; Sensors; Servers; Smart cities; Spatial data; Unmanned aerial vehicles; Unmanned autonomous vehicles; Wireless communication; Wireless sensor networks
• ISSN: 0890-8044; 1558-156X
• DOI: 10.1109/MNET.2019.1800214

As air pollution is becoming the largest environmental health risk, the monitoring of air quality has drawn much attention in both theoretical studies and practical implementations. In this article, we present a real-time, fine-grained, and power-efficient air quality monitor system based on aerial and ground sensing. The architecture of this system consists of the sensing layer to collect data, the transmission layer to enable bidirectional communications, the processing layer to analyze and process the data, and the presentation layer to provide a graphic interface for users. Three major techniques are investigated in our implementation for data processing, deployment strategy, and power control. For data processing, spatial fitting and short-term prediction are performed to eliminate the influences of incomplete measurement and the latency of data uploading. The deployment strategies of ground sensing and aerial sensing are investigated to improve the quality of the collected data. Power control is further considered to balance between power consumption and data accuracy. Our implementation has been deployed in Peking University and Xidian University since February 2018, and has collected almost 100,000 effective values thus far.