| Title | AQNet: Fine-grained 3D spatio-temporal air quality monitoring by aerial-ground WSN |
| Authors | Yang, Yuzhe Bai, Zixuan Hu, Zhiwen Zheng, Zijie Bian, Kaigui Song, Lingyang |
| Affiliation | School of Electrical Engineering and Computer Science, Peking University, Beijing, China |
| Issue Date | 2018 |
| Publisher | 2018 IEEE Conference on Computer Communications Workshops, INFOCOM 2018 |
| Citation | 2018 IEEE Conference on Computer Communications Workshops, INFOCOM 2018. 2018, 1-2. |
| Abstract | This demo presents AQNet, an aerial-ground wireless sensor network (WSN) system, for fine-grained air quality monitoring and forecasting in urban three-dimensional (3D) area. AQNet contains 200 programmable on-ground PM2.5 sensors for 2D baseline monitoring, and an unmanned-aerial-vehicle (UAV) with the same sensor for air quality profiling at different heights. These low-cost sensors are programmed to wake up between adjustable time intervals, record and send real-time PM2.5 data back to the central server for data fusion. A learning model is proposed to utilize the data in both spatio-temporal perspectives to estimate PM2.5 at unmeasured locations and forecast the air quality distribution in the near future. Further, the collected data is also used to control and optimize the UAV's monitoring operation. For the convenience of user queries, we present the PM2.5 map by a website-based GUI for real-time visualization. AQNet has been realized and deployed on campus of Peking University, and is scalable and energy-efficient to be extended to larger and more dedicated areas. © 2018 IEEE. |
| URI | http://hdl.handle.net/20.500.11897/530854 |
| ISSN | 9781538659793 |
| DOI | 10.1109/INFCOMW.2018.8406985 |
| Indexed | EI |
| Appears in Collections: | 信息科学技术学院 |
