Retrofitting smartphones to be used as particulate matter dosimeters

Budde, Matthias; Barbera, Pierre; Masri, Rayan El; Riedel, Till; Beigl, Michael

doi:10.1145/2493988.2494342

Cited by 16 publications

(19 citation statements)

References 6 publications

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“…In parallel to the work presented in the paper at hand, we worked on integrating an optical dust sensor with mobile phones. The general feasibility of an approach to use the camera and LED ash as the receptor respectively the light source of such a sensor has been shown in [5]. Most of this work's results are applicable to such a system as well.…”

Section: Related Workmentioning

confidence: 83%

Enabling low-cost particulate matter measurement for participatory sensing scenarios

Budde

Masri

Riedel

et al. 2013

Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia

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Abstract. This paper presents a mobile, low-cost particulate matter sensing approach for the use in Participatory Sensing scenarios. It shows that cheap commercial o-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 tted 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-y 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-y cross-device data processing have been done.

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Section: Related Workmentioning

confidence: 83%

Enabling low-cost particulate matter measurement for participatory sensing scenarios

Budde

Masri

Riedel

et al. 2013

Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia

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“…We selected data from two separate real-world projects to test the feasibility for different phenomena: Particulate matter sensing with camera-phone dust sensors (MobileDust [4]) and low-cost gas sensing (OpenSense [13]). The MobileDust dataset was recorded in a lab setting with a Google Nexus 4 smartphone and a clip-on dust sensor that is integrated in a removable back-cover (see Figure 1).…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore the standard deviation of the fit parameter A from its meanĀ = 16414.8 is σ A = 1874. 4 in adjusting the fit parameters between different measurement series, which is a measure for stability of the calibration approach.…”

Section: Discussionmentioning

confidence: 99%

“…with clip-on approaches [4], the changing integrity of the sensing system is another source of errors. In camera-based sensing, a user could e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Our approach is robust against both static and dynamic baseline shifts (offset and drift), as well to a certain extent against cross-sensitivity. We demonstrate the feasibility on data from clip-on particulate matter sensors for cameraphones [4] and from low-cost ozone sensing [13].…”

Section: Introductionmentioning

confidence: 99%

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Budde

Köpke

Beigl

2015

Proceedings of the 2015 ACM International Symposium on Wearable Computers - ISWC '15

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Abstract-Personal and participatory environmental sensing, especially of air quality, is a topic of increasing importance. However, as the employed sensors are often cheap, they are prone to erroneous readings, e.g. due to sensor aging or low selectivity. Additionally, non-expert users make mistakes when handling equipment. We present an elegant approach that deals with such problems on the sensor level. Instead of characterizing systematic errors to remove them from the noisy signal, we reconstruct the true signal solely from its Poisson noise. Our approach can be applied to data from any phenomenon that can be modeled as particles and is robust against both offset and drift, as well to a certain extent against cross-sensitivity. We show its validity on two real-world datasets.

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Informing decisions in agent-based models — A mobile update

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Retrofitting smartphones to be used as particulate matter dosimeters

Cited by 16 publications

References 6 publications

Enabling low-cost particulate matter measurement for participatory sensing scenarios

Enabling low-cost particulate matter measurement for participatory sensing scenarios

Robust in-situ data reconstruction from poisson noise for low-cost, mobile, non-expert environmental sensing

Informing decisions in agent-based models — A mobile update

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