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

Budde, Matthias; Köpke, Marcel; Beigl, Michael

doi:10.1145/2802083.2808406

Cited by 11 publications

(8 citation statements)

References 14 publications

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“…The Poisson Particle Detection (PPD) algorithm has been proposed by us in previous work as a simple signal reconstruction scheme for data from environmental phenomena that can be modeled as particles [29]. It was designed to reconstruct the “true” signal from noise-afflicted data solely from the Poisson noise of a signal.…”

Section: The Feinphone Systemmentioning

confidence: 99%

“…In a Poisson process, there is signal dependent noise: The number of observed occurrences fluctuates with a standard deviation of σn=n¯ around its mean n¯. From this noise a reconstruction of the mean concentration of the signal can be calculated while at the same time removing systematic measurement error [29]. The PPD algorithm is shown in Algorithm 2.…”

Section: The Feinphone Systemmentioning

confidence: 99%

“…In past research, we applied the PPD algorithm directly to the sum signal of a light scattering particle sensor [29]. In this work, we combine it with the counting algorithm and the PPD algorithm.…”

Section: The Feinphone Systemmentioning

confidence: 99%

“…( a ) Scatter blob [29] vs. ( b ) individual scatter traces, for illustrative purposes both at high particle concentrations.Scatter patterns: blob v. individual traces…”

Section: Figurementioning

confidence: 99%

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FeinPhone: Low-cost Smartphone Camera-based 2D Particulate Matter Sensor

Budde

Leiner

Köpke

et al. 2019

Sensors

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Precise, location-specific fine dust measurement is central for the assessment of urban air quality. Classic measurement approaches require dedicated hardware, of which professional equipment is still prohibitively expensive (>10k$) for dense measurements, and inexpensive sensors do not meet accuracy demands. As a step towards filling this gap, we propose FeinPhone, a phone-based fine dust measurement system that uses camera and flashlight functions that are readily available on today’s off-the-shelf smart phones. We introduce a cost-effective passive hardware add-on together with a novel counting approach based on light-scattering particle sensors. Since our approach features a 2D sensor (the camera) instead of a single photodiode, we can employ it to capture the scatter traces from individual particles rather than just retaining a light intensity sum signal as in simple photometers. This is a more direct way of assessing the particle count, it is robust against side effects, e.g., from camera image compression, and enables gaining information on the size spectrum of the particles. Our proof-of-concept evaluation comparing several FeinPhone sensors with data from a high-quality APS/SMPS (Aerodynamic Particle Sizer/Scanning Mobility Particle Sizer) reference device at the World Calibration Center for Aerosol Physics shows that the collected data shows excellent correlation with the inhalable coarse fraction of fine dust particles (r > 0.9) and can successfully capture its levels under realistic conditions.

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Section: The Feinphone Systemmentioning

confidence: 99%

Section: The Feinphone Systemmentioning

confidence: 99%

Section: The Feinphone Systemmentioning

confidence: 99%

“…( a ) Scatter blob [29] vs. ( b ) individual scatter traces, for illustrative purposes both at high particle concentrations.Scatter patterns: blob v. individual traces…”

Section: Figurementioning

confidence: 99%

See 2 more Smart Citations

FeinPhone: Low-cost Smartphone Camera-based 2D Particulate Matter Sensor

Budde

Leiner

Köpke

et al. 2019

Sensors

Self Cite

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“…With low-cost sensors, the systematic error may increase with time, e.g. due to sensor aging or other causes [14]. Some sensors, e.g.…”

Section: Approachmentioning

confidence: 99%

Privacy-Preserving Collaborative Blind Macro-Calibration of Environmental Sensors in Participatory Sensing

Markert¹,

Budde²,

Schindler³

et al. 2017

EAI Endorsed Trans IoT

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The ubiquity of ever-connected smartphones has lead to new sensing paradigms that promise environmental monitoring in unprecedented temporal and spatial resolution. Everyday people may use low-cost sensors to collect environmental data. However, measurement errors increase over time, especially with low-cost air quality sensors. Therefore, regular calibration is important. On a larger scale and in participatory sensing, this needs be done in-situ. Since for this step, personal sensor data, time and location need to be exchanged, privacy implications arise. This paper presents a novel privacy-preserving multi-hop sensor calibration scheme, that combines Private Proximity Testing and an anonymizing MIX network with cross-sensor calibration based on rendezvous. Our evaluation with simulated ozone measurements and real-world taxicab mobility traces shows that our scheme provides privacy protection while maintaining competitive overall data quality in dense participatory sensing networks.

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Budde

2021

Springer Handbooks

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

Cited by 11 publications

References 14 publications

FeinPhone: Low-cost Smartphone Camera-based 2D Particulate Matter Sensor

FeinPhone: Low-cost Smartphone Camera-based 2D Particulate Matter Sensor

Privacy-Preserving Collaborative Blind Macro-Calibration of Environmental Sensors in Participatory Sensing

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