NoiseTube: Participatory sensing for noise pollution via mobile phones

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Participatory Sensing for urban polllution Nicolas Maisonneuve – Associate Researcher SONY Computer science Laboratory Paris Sep - 2009 a new role for citizens a new instrument to observe population/local communities exposure

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Participatory sensing for urban pollution Research on a participatory approach empowering people in the monitoring of noise pollution via their mobile phones to collect their environmental conditions and cartography their collective exposure http://noiseTube.net

Transcript of NoiseTube: Participatory sensing for noise pollution via mobile phones

Page 1: 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

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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“Agricultural statistics has deteriorated over time” - weak estimation of global rice/wheat productions- fisheries data outdated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]

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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

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#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

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• 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

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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)

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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

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How does it works?

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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

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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?

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New tagging usage: People as semantic sensors for pollution

Great but limited (amount of) contextual information

Challenge 2: Contextualizing environmental data

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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

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Real-time collective exposure

Challenge 3: visualisation

Google Earth+ Web-based

• Exposure layer• Semantic layer • Contextual information • Contribution layer

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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

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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

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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