NoiseTube project


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  • Goal: Empowering citizens to monitor local knowledge using mobile devices The goal of my reseach is : to tackle two important problems related to Environmental Management. ------ The first one is the current lack of fine-grained data 1/ 2/ Involve citizens in the observation/assessment of environmental issues. And how a participatory sensing approach , which is local knowledge can be used ----- -> first at all , I want to explain quickly why these two issues are important?
  • We know the need for data is growing to battle (global warming, agriculture, safe drinking water) and the need to update them quickly to is crucial aspect to readapt scientific knowledge/model and thus global or local policies. information-gathering is getting worse, according to several interviews and audit reports. 1/ Water: officials also have a broad notion of the amount of fresh water on the planet. 2/ Food: the latest fisheries report uses 2006 data. In 2007, annual rice production was estimated for 10 of 16 Asian nations surveyed. 3/ In Health, we have a lot of uncertainly about the exposure of people to pollution, because of the complexity to collect fine–grained data A lot of reports are base on statistics very doubt, what can we do with so uncomplete, uncertaint or event missing data
  • The second problem related to environmental management is the current role of citizens. we know a lot of environmental issues are directly related to the human activity. The change of behavior is one of the key of the pb So by trying to involve them in this management, we support the change of behavior. But in the reality few and local initiatives despite the recognition of a lot of international agreements So How could they participate , not only at decision level , but also at the assessment level
  • To test this approach , we took the pb of noise pollution monitoring Limited modeling: Limited sources of noise (diversity?) Focus on noise emission, not noise exposure (population?) Static “average day” for 5 years (unusual event?) Lack of data/observation: outdated/estimated proxy input data (traffic in 2000) No validation in complex situation, no real data
  • Elog: environmental dimension of the life of the people Easy data discovery, subscription, and republication is crucial.
  • PlaceEngine?
  • Continuer / We are at the very early learning stages of this work with much excitement still to come. Potential / new
  • Full-cycle information The measurement that means the most to you is likely to be the one that captures the actual conditions you are currently experiencing, not citywide averages.
  • NoiseTube project

    1. 1. Participatory sensing The NoiseTube project Nicolas Maisonneuve, Associate Researcher, SONY CSL Paris
    2. 2. Issue 1: Scarcity of fine-grained data Issue 2: Limited role of citizens Environmental management <ul><li>Change of paradigm in environmental data production </li></ul><ul><li>Citizens in the loop </li></ul>Environmental/ health Sciences Social /political sciences Participatory sensing Research
    3. 3. Data gathering: a general problem Water : limited accurate info on water and treatment systems Food: Agricultural statistics deteriorated over time Health: uncertainty in e xposure 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. 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. 5. Opportunity for Participatory Sensing <ul><li> New usage of phone </li></ul><ul><li>phone = personal measurement instrument </li></ul><ul><li> Individual level : new user experience </li></ul><ul><li>Autonomy to measure local phenomena </li></ul><ul><li>New form of expression/collaboration </li></ul><ul><li>Collective level: adaptative sensor network with a limited cost </li></ul>Empowerment in the digital world Growing public concern + + Democratization of powerful & sensor-rich phones
    6. 6. Case study: noise pollution monitoring in EU EU countries Health: 170 Million citizens impacted Economical cost: €12 billion issues <ul><li>Limited modeling </li></ul><ul><li>Lack of observation </li></ul><ul><li>Emission vs exposure </li></ul><ul><li>EU Call for real data! </li></ul><ul><li>“ Goals for future research include supplying the missing data .” [EC, 2004] </li></ul><ul><li>“ Every effort should be made to obtain accurate real data “ [EC WG, 2006] </li></ul><ul><li> No real exposure data at the individual level </li></ul>Reality Noise map simulation VS.
    7. 7. Participatory monitoring of noise pollution using mobile phones 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. 8. How does it work? Human-based sensing No sound sent, only exposure  User Privacy, user experience, Scalability <ul><li>LAeq(1s) </li></ul><ul><li>computation (SP) </li></ul><ul><li>Exposure Pattern recognition </li></ul>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. 9. But, what about the accuracy? Real-world experiment Park Experiment In lab Person equipped with sensors Phone + hand free kit Professional sensors ? = Virtual noise sensor = microphone + software Sound Level Meter (200$) Collaboration with After correction: error 2 db
    10. 10. But, what about the accuracy? ? = Virtual noise sensor = microphone + software Sound Level Meter (200$) Real-world experiment Experiment 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. 11. And measuring is not enough.. Hazard identification issues Difficult to identify & separate noise sources from exposure recordings O pportunity: 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. 12. Real-time collective exposure Simple visualisations for citizens/ police makers <ul><li>Exposure layer </li></ul><ul><li>Semantic layer </li></ul><ul><li>Contextual information </li></ul><ul><li>Real-time </li></ul>Data aggregation by small piece of street
    13. 13. <ul><ul><li>Connected to the web ecosystem </li></ul></ul><ul><ul><li>Data commons accessible via public web API (GeoRSS, JSON) (e.g. tracking new data in an area) </li></ul></ul><ul><ul><li>Connected to the people </li></ul></ul><ul><ul><li>Real-time s preading of environmental information through social network service </li></ul></ul><ul><ul><li> Enhancing the conversation & dissemination (push) </li></ul></ul>Multiple ways to diffusion information (in progress) SMS
    14. 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. 15. <ul><ul><li>Empowerment - case study 2: Mumbai, India (2009) </li></ul></ul><ul><ul><li>Collaboration with NGO Awaaz Foundation organizing a campaign to raise awareness and map Mumbai, (end of september) </li></ul></ul><ul><ul><li>(need to adapt the application to the indian phone market) </li></ul></ul>3rd most noisy city in the world AFP news
    16. 16. vs <ul><li>Make it real (for real world experimentation) </li></ul><ul><li>No need new device: Recycling phone </li></ul><ul><li>1st project accessible to the public (June) </li></ul><ul><li>1st subway noise map </li></ul><ul><li>Data collected </li></ul><ul><li>Personal and geolocated exposure data (epidemiological studies) </li></ul><ul><li>Human-based sensing </li></ul><ul><li>Visualization & Sharing </li></ul><ul><li>Data aggregation by urban element </li></ul><ul><li>(future) pollution information spreaded via social networks (twitter) </li></ul>Related projects for air pollution « sensing atmosphere  (taxi driver) Berkeley, 2007 Noisetube “ Mobile Urban Sensing project” Cambridge, 2008 « street sweeper », Berkeley, 2008 Conclusion
    17. 17. <ul><li>Thanks for your attention </li></ul><ul><li>Any question? </li></ul><ul><li>Nicolas Maisonneuve </li></ul>