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NoiseTube: Participatory sensing for noise pollution via mobile phones
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Transcript of NoiseTube: Participatory sensing for noise pollution via mobile phones
Participatory Sensing for urban polllution
Nicolas Maisonneuve – Associate Researcher SONY Computer science Laboratory Paris
Sep - 2009
a new role for citizensa new instrument to observe population/local communities exposure
Data gathering: a general problem
Water: “U.N. has a limited success to get accurate information on water infrastructure and treatment systems”
Food: “Agricultural statistics has deteriorated over time” - weak estimation of global rice/wheat productions- fisheries data outdated
Health: ”Exposure measures are sometimes completely lacking, frequently incomplete or otherwise uncertain”.
[Poor data, weak agencies hamstring U.N. environmental oversight, NY Times, 2009]
[Food and Agriculture Organization, Audit 2009]
[Uncertainty and Data Quality in Exposure Assessment, Wolrd Health Organization, 2008]
Noise/air pollution monitoring
• Important environmental issues in cities• (long term) health, social and economic impacts• An increasing problem, especially in developing countries
• Growing public concern & effort (European Directive -2002)• but limited success of environmental policies Complexity of monitoring the real exposure of the population
Air pollution- Los Angeles Noise pollution in Mumbai
#1 issue: Lack of real exposure data of people
• Modeling emission (not exposure)
• Uncertainty of the results• Real-time: hazard detection?• Cost
• Sparsity (Paris: 6 sensors for noise, 10 sensors for air quality)
• Location-based exposure (not population)• Cost
Emission modeling + Sensor network
noise map of ParisFew sensors in Paris
Noise/air pollution monitoring
• Urban pollution = anthropogenic effect
• No real citizen participation despite international agreements
#2 issue: Limited role of citizens in pollution management
“Environmental issues are best handled with the participation of all concerned citizens..” [Principle 10, Rio Declaration, 1992]
Needing to involve the public in the debate :
to get a better representation of their environmental conditions
To interact in a more direct and powerful way
Noise/air pollution monitoring
NoiseTube Project: New green user experience
• Citizens in the loop: reporting directly their environmental conditions• Building collective maps of their shared exposure to noise
Supplying real exposure data
What if every mobile device had an noise (air) sensor?
Issue #1 - Environmental/ health Sciences
Issue #2 - Social/political sciences
• Low cost adaptive sensor network• Collecting fine-grained real data
Citizen empowerment
• Phone = low cost measurement device• Personalized environmental information
(health device)
Why now?Opportunity of P.S. in environmental context
Cultural shift in digital world (Web 2.0)
Growing public concern
+ + Democratization of powerful
& rich-sensor phones
Transferring production & collaboration practices from the digital world (web2.0) into the physical world by providing simple tools to observe environmental issues using today mobile devices
Autonomy/freedom (no need to wait official/expert)New opportunities for public discourse
How does it works?
Challenge 1: accuracy
Phone as noise sensor
Signal processing algorithm to compute Leq(A)
+
Experiments to evaluate accuracy
Phone in hand Handsfree kit Phone in pocket
± 2.5 dB ± 4.5 dB ± 6.5 dB
LeqA-weighted filter
+
Phone specific correction function
Challenge 2: Contextualizing environmental data
Why do we need the context? add meaning to raw data
Only measurements, No semantic information
Simulated mapMeasurement done by real sensors
2- Hard to identify the source of pollution with only numerical data
1- Hard to search in numerical datasets for humansMeaning of 75 dB(A): bad /good? Lat,Lng={2.34, 12.5}: which street?
New tagging usage: People as semantic sensors for pollution
Great but limited (amount of) contextual information
Challenge 2: Contextualizing environmental data
Machine tagging: Enriching the context with classifiersRoadwork Neighbors
Loudness Signal Pattern
Location
Time
Weather
Location type
City NameStreet name
Noise Exposure
DayWeek Season
WindsType
Temperature
User ActivityMobility
Challenge 2: Contextualizing environmental data
Real-time collective exposure
Challenge 3: visualisation
Google Earth+ Web-based
• Exposure layer• Semantic layer • Contextual information • Contribution layer
Challenge 4: Sharing
Connected to the people
ELog: Environmental log “See the digital traces of my exposure to pollution“
New Grid for personal environmental information: Sprending environmental information through Social Network (Twitter) Widget on blog
Citizens empowermentCase study: Exposure to noise in mass transit system
“recent [US] public health studies have identified several sources of environmental hazards associated with mass transit, including excessive noise, a large and growing problem in urban settings” ( Science daily June 2009)
Paris Subway - 2008No public information about exposure to noiseBuilding exposure map of 2 lines
Conclusion NoiseTube: Participatory model to monitor noise
pollution using mobile phones
• New green user experience • “Elog” (Exposure log): Reporting and sharing personal exposure to the
community• Low cost adaptive sensor network supplying real exposure data
Future work Experimentation: BruitParif, open Lab , Brussels, India, Italy) Data quality of peer production system in the physical world Injecting semantics to transform large raw data into actionable knowledge Mechanism to support cooperation / collective action