Name
Abstract | ||
|---|---|---|
This demo presents MegaSense, an air pollution monitoring system for realizing low-cost, near real-time and high resolution spatio-temporal air pollution maps of urban areas. MegaSense involves a novel hierarchy of multi-vendor distributed air quality sensors, in which accurate sensors calibrate lower cost sensors. Current low-cost air quality sensors suffer from measurement drift and they have low accuracy. We address this significant open problem for dense urban areas by developing a calibration scheme that detects and automatically corrects drift. MegaSense integrates with the 5G cellular network and leverages mobile edge computing for sensor management and distributed pollution map creation. We demonstrate MegaSense with two sensor types, a state of the art air quality monitoring station and a low-cost sensor array, with calibration between the two to improve the accuracy of the low-cost device. Participants can interact with the sensors and see air quality changes in real-time, and observe the mechanism to mitigate sensor drift. Our re-calibration method minimizes the error for NO2 and O3 81% of the time (vs single calibration) and reduces the mean relative error by 25%-45%. |
Year | Venue | Field |
|---|---|---|
2018 | MobiCom | Edge computing,Computer science,Sensor array,Real-time computing,Sensor fusion,Mobile edge computing,Air quality index,Cellular network,Approximation error,Calibration,Distributed computing |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eemil Lagerspetz | 1 | 427 | 29.56 |
| Samu Varjonen | 2 | 6 | 2.65 |
| Francesco Concas | 3 | 2 | 0.74 |
| Julien Mineraud | 4 | 29 | 6.15 |
| Sasu Tarkoma | 5 | 1312 | 125.76 |