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Step 4Future research and conclusion
True to the "beta" spirit of Web 2.0 we decided to open our platform to everyone, despite the early stage of development. In the near future updated versions of our tools will offer improved and new features. Our research and development will continue along several tracks:
Calibration
Without proper calibration, sensor devices produce data that may not be representative or can even be misleading. So how can we calibrate hundreds of different mobile phone types or other sound recorders without using an expensive sound level meter each time? We propose to investigate such research questions by different tracks, where calibrated phones or acoustical stable locations can be used as reference points to automatically (re)calibrate a phone (e.g. calibration between 2 phones, connected through Bluetooth, where one is the reference and the other is the phone to calibrate).
Indoor localization
The GPS system does virtually not support indoor localization. Because most people spend a lot of their daily lives indoors this is an important shortcoming which we have partially solved through manual localization (see step 2). However, there are technologies which can act as alternatives for GPS in indoor scenario's. One of the more promising (and widely studied) approaches is GSM-based positioning. Such technologies could be especially helpful to investigate noise in subway (such as Paris' Metro network), which are known to be very noisy environments. We have already done some experimentation with temporal markers and a reconstruction of locations by interpolation (see figure). However, by employing GSM-based positioning (identifying antennas in different stations, to automatically detect the location of the user), we expect we will be able to produce more accurately localized measurements in this special environment in the future.
Social aspect: Community building
Projecting noise pollution data onto maps is the common feature. But recording sound exposure from the people's activity allows also us to gather a kind of data which is more people-centric and not only place-centric data which is collected by traditional static sound level meters put in streets. From this observation we will look into more social-related features. For instance, creating personal noise profiles containing your noise exposure in temporal and geographic dimensions and a list of your own tagged sources of noise, providing a way to compare people and find similar profiles in order to support collective action.
Conclusion
In this "Instructable" we have presented a new way to monitor and map of noise pollution thanks to the participation of the people. The NoiseTube platform enables you to contribute to a distributed noise measurement campaign using your mobile phone. This platform is still under heavy development and the near future will bring further improvements. However, we would like to invite you to join the NoiseTube community and try out our software.
If you have any questions, suggestions or other comments, please do not hesitate to contact us or react through the comments on this Instructable. Furthermore we would like to stress that we are open to collaborate with both public or research organizations.
Further reading
To find out more and stay informed about the NoiseTube project please visit our website at www.noisetube.net. If you would like to read up on the scientific background of this work please refer to these papers:
This project work was partially supported by the EU under contract IST-34721 (TAGora). The TAGora project is funded by the Future and Emerging Technologies program (IST-FET) of the European Commission. Matthias Stevens is a research assistant of the Fund for Scientific Research, Flanders (Aspirant van het Fonds Wetenschappelijk Onderzoek - Vlaanderen).
Calibration
Without proper calibration, sensor devices produce data that may not be representative or can even be misleading. So how can we calibrate hundreds of different mobile phone types or other sound recorders without using an expensive sound level meter each time? We propose to investigate such research questions by different tracks, where calibrated phones or acoustical stable locations can be used as reference points to automatically (re)calibrate a phone (e.g. calibration between 2 phones, connected through Bluetooth, where one is the reference and the other is the phone to calibrate).
Indoor localization
The GPS system does virtually not support indoor localization. Because most people spend a lot of their daily lives indoors this is an important shortcoming which we have partially solved through manual localization (see step 2). However, there are technologies which can act as alternatives for GPS in indoor scenario's. One of the more promising (and widely studied) approaches is GSM-based positioning. Such technologies could be especially helpful to investigate noise in subway (such as Paris' Metro network), which are known to be very noisy environments. We have already done some experimentation with temporal markers and a reconstruction of locations by interpolation (see figure). However, by employing GSM-based positioning (identifying antennas in different stations, to automatically detect the location of the user), we expect we will be able to produce more accurately localized measurements in this special environment in the future.
Social aspect: Community building
Projecting noise pollution data onto maps is the common feature. But recording sound exposure from the people's activity allows also us to gather a kind of data which is more people-centric and not only place-centric data which is collected by traditional static sound level meters put in streets. From this observation we will look into more social-related features. For instance, creating personal noise profiles containing your noise exposure in temporal and geographic dimensions and a list of your own tagged sources of noise, providing a way to compare people and find similar profiles in order to support collective action.
Conclusion
In this "Instructable" we have presented a new way to monitor and map of noise pollution thanks to the participation of the people. The NoiseTube platform enables you to contribute to a distributed noise measurement campaign using your mobile phone. This platform is still under heavy development and the near future will bring further improvements. However, we would like to invite you to join the NoiseTube community and try out our software.
If you have any questions, suggestions or other comments, please do not hesitate to contact us or react through the comments on this Instructable. Furthermore we would like to stress that we are open to collaborate with both public or research organizations.
Further reading
To find out more and stay informed about the NoiseTube project please visit our website at www.noisetube.net. If you would like to read up on the scientific background of this work please refer to these papers:
- Nicolas Maisonneuve, Matthias Stevens, Maria Niessen, Peter Hanappe and Luc Steels. NoiseTube: Measuring and mapping noise pollution with mobile phones. Submitted to 4th International Symposium on Information Technologies in Environmental Engineering(ITEE 2009), Thessaloniki, Greece. May 28-29, 2009. Under review. PDF
- Nicolas Maisonneuve, Matthias Stevens, Maria Niessen, Peter Hanappe and Luc Steels. Citizen Noise Pollution Monitoring. Submitted to 10th Annual International Conference on Digital Government Research(dg.o2009), Puebla, Mexico, May 17-20, 2009. Under review. PDF
- J. Burke, D. Estrin, M. Hansen, A. Parker, N. Ramanathan, S. Reddy and M. B. Srivastava. ''Participatory Sensing''. In ''ACM Sensys World Sensor Web Workshop''. ACM Press, 2006.
- Cuff D., Hansen M. and Kang J. Urban Sensing: out of the woods. Communications of the ACM, 51(3), pp. 24-33, March 2008, ACM Press.
- J. Hellbruck, H. Fastl and B. Keller. Does meaning of sound influence loudness judgements?. In Proceedings of the 18th International Congress on Acoustics (ICA 2004). Pages 1097-1100.
- D. Menzel, H. Fastl, R. Graf and J. Hellbruck. Influence of vehicle color on loudness judgments. In Journal Of The Acoustical Society Of America, May 2008, 123(5), pages 2477-2479.
- Paulos, E. et al. Citizen Science: Enabling Participatory Urbanism. In Hand-book of Research on Urban Informatics: The Practice and Promise of the Real-Time City, Marcus Foth (Ed.), pp. 414-436, Idea Group, 2008.
- L. Yu and J. Kang. Effects of social, demographical and behavioral factors on the sound level evaluation in urban open spaces. In Journal of the Acoustical Society of America, February 2008, 123(2), pages 772-783.
This project work was partially supported by the EU under contract IST-34721 (TAGora). The TAGora project is funded by the Future and Emerging Technologies program (IST-FET) of the European Commission. Matthias Stevens is a research assistant of the Fund for Scientific Research, Flanders (Aspirant van het Fonds Wetenschappelijk Onderzoek - Vlaanderen).
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