Name
Abstract | ||
|---|---|---|
This paper presents a mobile, low-cost particulate matter sensing approach for the use in Participatory Sensing scenarios. It shows that cheap commercial off-the-shelf (COTS) dust sensors can be used in distributed or mobile personal measurement devices at a cost one to two orders of magnitude lower than that of current hand-held solutions, while reaching meaningful accuracy. We conducted a series of experiments to juxtapose the performance of a gauged high-accuracy measurement device and a cheap COTS sensor that we fitted on a Bluetooth-enabled sensor module that can be interconnected with a mobile phone. Calibration and processing procedures using multi-sensor data fusion are presented, that perform very well in lab situations and show practically relevant results in a realistic setting. An on-the-fly calibration correction step is proposed to address remaining issues by taking advantage of co-located measurements in Participatory Sensing scenarios. By sharing few measurement across devices, a high measurement accuracy can be achieved in mobile urban sensing applications, where devices join in an ad-hoc fashion. A performance evaluation was conducted by co-locating measurement devices with a municipal measurement station that monitors particulate matter in a European city, and simulations to evaluate the on-the-fly cross-device data processing have been done. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2541831.2541859 | MUM |
Keywords | Field | DocType |
participatory sensing scenario,cheap cots sensor,high measurement accuracy,gauged high-accuracy measurement device,low-cost particulate matter measurement,mobile phone,municipal measurement station,co-located measurement,co-locating measurement device,mobile personal measurement device,bluetooth-enabled sensor module,air quality,wearable,particulate matter,pm10 | Data processing,Particulates,Wearable computer,Computer science,Simulation,Sensor fusion,Real-time computing,Human–computer interaction,Mobile phone,Accuracy and precision,Participatory sensing,Calibration | Conference |
Citations | PageRank | References |
8 | 0.83 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthias Budde | 1 | 175 | 23.08 |
| Rayan El Masri | 2 | 15 | 1.76 |
| T. Riedel | 3 | 252 | 35.74 |
| M. Beigl | 4 | 2034 | 311.09 |