Software Campus: FeinPhone
March 4th, 2015 | Published in Research
The ubiquity of mobile networks and smartphones in principle offers an excellent infrastructure for participatory environmental measurements, e.g. in Smart City scenarios. Modern smartphones are already equipped with a variety of internal sensors. In some current models, even environmental sensors for measuring temperature, humidity and air pressure can be found. However, phenomena such as the air quality so far only can be detected with dedicated devices that measure stand-alone or communicate their sensor readings to a smartphone. This places additional cost and maintainance burdens on the user.
On the other hand, enabling particulate matter readings with smartphones has a large potential. The particulate matter air pollution in cities today is measured by expensive, static monitoring stations with poor spatial and temporal resolution. Distributed mobile measurements would enable applications such as fine-grained dynamic pollution maps, hotspot detection or reactive systems, e.g. for traffic control, in scenarios of future smart cities. These all require dense, distributed measurements. In order to reach this goal, this project aims to explore the measurement of particulate matter using off-the-shelf smartphone sensors and simple passive hardware addons.
The core tasks covered in this Software Campus project are to develop and evaluate a suitable clip-on sensor module for smartphones as well as an app and suitable algorithms for the processing of the recorded images on the smartphone.
Start/End
- 04/2015 – 05/2017
Partners
- Siemens AG
Research topics
- Environmental Sensing
- Mobile Computing
- Participatory Sensing
Contact
- Matthias Budde (email: budde(at)teco.edu)
Selected Publications
(2019) FeinPhone: Low-cost Smartphone Camera-based 2D Particulate Matter Sensor, Sensors 19(3), p. 749, pdf, doi:10.3390/s19030749
(2018) Potential and Limitations of the Low-Cost SDS011 Particle Sensor for Monitoring Urban Air Quality, ProScience 5(3rd International Conference on Atmospheric Dust (DUST2018)), p. 6-12, url, doi:10.14644/dust.2018.002
(2018) Suitability of the Low-Cost SDS011 Particle Sensor for Urban PM-Monitoring, Scientific Research Abstracts 8 (DUST 2018), p. 11, pdf
(2018) Privacy-Preserving Collaborative Blind Macro-Calibration of Environmental Sensors in Participatory Sensing, EAI Endorsed Transactions on Internet of Things 18(10), pdf, doi:10.4108/eai.15-1-2018.153564
(2017) Participatory Sensing or Participatory Nonsense? — Mitigating the Effect of Human Error on Data Quality in Citizen Science, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) 1(3), pdf, doi:10.1145/3131900
(2016) Design of a Light-scattering Particle Sensor for Citizen Science Air Quality Monitoring with Smartphones: Tradeoffs and Experiences, ProScience 3(2nd International Conference on Atmospheric Dust – DUST2016), p. 13-20, url, doi:10.14644/dust.2016.003
(2016) Sensified Gaming – Design Patterns and Game Design Elements for Gameful Environmental Sensing, 13th International Conference on Advances in Computer Entertainment Technology (ACE2016), ACM, pdf, doi:10.1145/3001773.3001832
(2016) Advances in Smartphone-based Fine Dust Sensing, II International Conference on Atmospheric Dust – DUST 2016 5, p. 23, pdf
(2015) Robust In-situ Data Reconstruction from Poisson Noise for Low-cost, Mobile, Non-expert Environmental Sensing, 19th International Symposium on Wearable Computers (ISWC'15), pdf, doi:10.1145/2802083.2808406