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| Content Provider | ACM Digital Library |
|---|---|
| Author | Cheng, Yun Jiang, Shouxu Jia, Ji Jiang, Xiaofan Li, Xiucheng Li, Yilong Li, Zhijun |
| Abstract | We present the design, implementation, and evaluation of AirCloud -- a novel client-cloud system for pervasive and personal air-quality monitoring at low cost. At the frontend, we create two types of Internet-connected particulate matter $(PM_{2:5})$ monitors -- AQM and miniAQM, with carefully designed mechanical structures for optimal air-flow. On the cloud-side, we create an air-quality analytics engine that learn and create models of air-quality based on a fusion of sensor data. This engine is used to calibrate AQMs and mini-AQMs in real-time, and infer $PM_{2:5}$ concentrations. We evaluate AirCloud using 5 months of data and 2 month of continuous deployment, and show that AirCloud is able to achieve good accuracies at much lower cost than previous solutions. We also show three real applications built on top of AirCloud by 3rd party developers to further demonstrate the value of our system. |
| Starting Page | 251 |
| Ending Page | 265 |
| Page Count | 15 |
| File Format | |
| ISBN | 9781450331432 |
| DOI | 10.1145/2668332.2668346 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-11-03 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Pm2.5 Air quality Client-cloud calibration system |
| Content Type | Text |
| Resource Type | Article |
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