NoiseTube project

1. Participatory sensing
The NoiseTube project
Nicolas Maisonneuve,
Associate Researcher, SONY CSL Paris

2. Issue 1: Scarcity of
fine-grained data
Issue 2: Limited
role of citizens
Environmental
management
Change of paradigm in environmental data production
Citizens in the loop
Environmental/
health Sciences
Social/political
sciences
Participatory sensing
Research

3. Data gathering: a general problem
Water: limited accurate info on
water and treatment systems
Food: Agricultural statistics
deteriorated over time
Health: uncertainty in
exposure assessment
[Poor data, weak agencies
hamstring U.N. environmental
oversight, NY Times, 2009]
[FAO, Audit 2009]
[Uncertainty and Data Quality
in Exposure Assessment,
WHO, 2008]
Poor system of monitoring

4. Why do we need to involve citizens?
“Environmental issues are
best handled with the
participation of all concerned
citizens..” [Principle 10, Rio
Declaration, 1992]
But, in reality.. no real participation despite
international agreements
Environmental issues = anthropogenic effect
Involvement  change of behavior

5. Opportunity for Participatory Sensing
Empowerment
in the digital world
Growing public
concern
+ +
Democratization of powerful
& sensor-rich phones
New usage of phone
phone = personal measurement instrument
Individual level : new user experience
Autonomy to measure local phenomena
New form of expression/collaboration
Collective level: adaptative sensor network
with a limited cost

6. Case study: noise pollution monitoring in EU
EU countries
Health: 170 Million citizens impacted
Economical cost: €12 billion
issues Limited modeling
 Lack of observation
 Emission vs exposure
EU Call for real data!
 “Goals for future research include supplying the missing data.” [EC, 2004]
 “Every effort should be made to obtain accurate real data“ [EC WG, 2006]
 No real exposure data at the individual level
Reality
Noise map simulation
VS.

7. Participatory monitoring of noise
pollution using mobile phoneswww.noisetube.net
Goal: Enabling citizens to measure and tag their everyday
exposure to inform the community by:
 supplying real exposure data
 building a collaborative exposure map of their
environment

8. How does it work?
Human-based sensing
No sound sent, only exposure
 User Privacy, user experience, Scalability
• LAeq(1s)
computation (SP)
• Exposure Pattern
recognition
sensors (microphone, gps)computer+
Phone-based sensing
free tagging of
exposure
Diffusion of Raw
exposure data
Bi-directional service
Collaborative exposure map
NoiseTube serve
Public data commons
Data postprocessing (GIS)
Gis server

9. But, what about the accuracy?
Real-world experimentExperiment In lab
Person equipped with sensors
Phone + hand free kit
Professional sensors
?
=
Virtual noise sensor =
microphone + software
Sound Level Meter
(200$)
Collaboration with
After correction: error 2 db

10. But, what about the accuracy?
?
=
Virtual noise sensor =
microphone + software
Sound Level Meter
(200$)
Real-world experimentExperiment In lab
Phone in Hand Hand free kit Phone in pocket
+/- 2,5 db +/- 4,5 db
+/- 6,5 db
correction in lab
phone
correction
dB
dB

11. And measuring is not enough..
Hazard identification issues
Difficult to identify & separate noise
sources from exposure recordings
Opportunity: tagging exposure
which source generated these levels?
Noise map
Sound level meter
map with a semantic layer
People are excellent at recognizing
noise sources
 Using people as sensors to tag the
source of their exposures (and thus
inform the community)

12. Real-time collective exposure
Simple visualisations for citizens/ police makers
•Exposure layer
•Semantic layer
•Contextual information
•Real-time
Data aggregation by small piece of
street

13. Connected to the web ecosystem
Data commons accessible via public web API
(GeoRSS, JSON) (e.g. tracking new data in an area)
Connected to the people
Real-time spreading of environmental information through
social network service
 Enhancing the conversation & dissemination (push)
Multiple ways to diffusion information (in progress)
SMS

14. Empowerment - case study 1: Subway in Paris
Location of the lines reconstructed afterwards (no GPS)
(+ PlaceEngine?)
No public information about noise in the subway
(private data owned by the RATP company)
(2008)

15. Empowerment - case study 2: Mumbai, India (2009)

●
Collaboration with NGO Awaaz Foundation organizing a campaign to
raise awareness and map Mumbai, (end of september)
●
(need to adapt the application to the indian phone market)
3rd most noisy city in the world
AFP news

16. vs
Make it real (for real world
experimentation)
•No need new device: Recycling phone
•1st project accessible to the public (June)
•1st subway noise map
Data collected
•Personal and geolocated exposure data
(epidemiological studies)
•Human-based sensing
Visualization & Sharing
•Data aggregation by urban element
•(future) pollution information spreaded
via social networks (twitter)
Related projects
for air pollution
« sensing atmosphere
(taxi driver) Berkeley, 2007
Noisetube
“Mobile Urban
Sensing project”
Cambridge, 2008
« street sweeper »,
Berkeley, 2008
Conclusion

17. Thanks for your attention
Any question?
Nicolas Maisonneuve
www.noisetube.net