NoiseTube: Participatory sensing for noise pollution via mobile phones


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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

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  • Personal exposure of urban pollution for large population is impractical” that common human diseases, such as asthma, cardiovascular disease, and cancer, result from a complex interplay between genes and environmental factors,valid estimates of individual exposure for large populationAnthropogenic sourcesTo make an educated decision you need data, good data. Unfortunatly, much of the environmental data cited by NGO’s, governments, and the private sector are incomplete, out of date or event estimated due to a complete lack.
  • First, I amgoing to briefly talk about the current state about noise pollution monitoring.We all know thaturban pollution in general (but noise pollution specially) is one of the main environmental issues in cities. Noise pollution both affect health, have a social impact (human behaviour). They also have an economical cost. (most visible aspect) Noise affects productivity (so having an economical impact to , especially for the noise) and especially the health of the people.(25 Billion of Euro / year in EU) Of course there are existingenvironmentalpoliciestrying to tacklethis issue. But they have not been reallysuccessfulso far. And one of the reasonsisbecause of the complexity to the assessment of the pollution. If you want to manage pollution , first you need to be able to monitor it. And currently there are a lot of uncertainty in the evaluation of the real exposure to pollution of the population.
  • Basically there are currently 2 official approaches to monitor noise (or air pollution).The first approach is to create pollution maps by modeling the emission of common sources of pollution, for instance the traffic.In this case we model the average level of emission of sources of pollution during a typical day using a lot of parameters. but it is not the real exposure of the people will walk for 3 minutes.On top of that such approach can’t monitor abnormal situation.The second approach is the use of a sensor networK.But currently the network are very sparce. There are only 6 sensors for noise and 7 for air quality. So, again, it’s very hard to evaluate at a fine grained the real exposure of the people. On top of that they are fixed. 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
  • A second reason of the limited success of current policies is the current role of the public. hey don’t involve the people in the pollution management. But because pollution is mainly made by the human activity. If they are a part of the problem, they should be a part of the solution. So we really need to involve the citizen in the pollution management, first to have a better representation fo their real environmental conditionsAnd secondly to act on the pollution by raising awareness in a more direct and powerful than just giving tradtional information at the city levelSo what can SONY do to support this issues?Involving people they raise awareness and change of behavior.Event if there are efforts in this direction , we need a novelway to raiseawareness moredirectly and in a much more powerfulwaythantraditional information at the city level
  • Participatory monitoring of noise pollution using mobile phonesWe developed the project NoiseTube. The ideas is to turn mobile phones into noise sensors.So the goal is not to only use phones as communication devices, multimedia devices or event localisation devices But also to add a new environmental dimension by using mobile phones as environmental instruments.By this way , we provide them a lost cost solution to measure their personal exposure . So to have a direct get personalied environmental information, which is a a much more powerful way to raise awareness than traditional information given by environmental agencies.On top of that , they can share they measurements with the community to inform it about their environmental conditions. Such approach enable the production of local environmental information from the public to the community and empower the citizens in the discussionof the environmental management by raising awareness about their environmental conditions.Furthremore by doing that, we create a mobiel and adaptive sensor network matching with the the real lifestyle and supplying real exposure data at the level of individual.
  • Credibility is important for the credibility
  • Add Tag cloud+ indicator
  • Such approach enable the production of local environmental information from the public to the community and empower the citizens in the debate of the environmental management by raising awareness about their environmental conditions.
  • NoiseTube: Participatory sensing for noise pollution via mobile phones

    1. 1. ParticipatorySensingfor urbanpolllution<br />a new role for citizens<br />a new instrument to observe population/local communitiesexposure<br />Nicolas Maisonneuve – Associate Researcher <br />SONY Computer science Laboratory Paris <br />Sep - 2009<br />
    2. 2. Data gathering: a generalproblem<br />Water: “U.N. has a limited success to get accurate information on water infrastructure and treatment systems”<br />[Poor data, weak agencies hamstring U.N. environmental oversight, NY Times, 2009]<br />Food: “Agricultural statistics has deteriorated over time” - weak estimation of globalrice/wheat productions<br />- fisheries data outdated<br />[Food and Agriculture Organization, Audit 2009]<br />Health: ”Exposure measures are sometimes completely lacking, frequently incomplete or otherwise uncertain”. <br />[Uncertainty and Data Quality in Exposure Assessment, Wolrd Health Organization, 2008]<br />
    3. 3. Noise/air pollution monitoring<br />Air pollution- Los Angeles<br />Noise pollution in Mumbai<br /><ul><li>Important environmental issues in cities
    4. 4. (long term) health, social and economic impacts
    5. 5. An increasing problem, especially in developing countries
    6. 6. Growing public concern & effort (European Directive -2002)
    7. 7. but limited success of environmental policies
    8. 8. Complexity of monitoring the real exposure of the population</li></li></ul><li>Noise/air pollution monitoring<br />#1 issue: Lack of real exposure data of people<br />Emission modeling + Sensor network<br />Few sensors in Paris<br /> noise map of Paris<br /><ul><li>Sparsity (Paris: 6 sensors for noise, 10 sensors for air quality)
    9. 9. Location-based exposure (not population)
    10. 10. Cost
    11. 11. Modeling emission (not exposure)
    12. 12. Uncertainty of the results
    13. 13. Real-time: hazard detection?
    14. 14. Cost</li></li></ul><li>Noise/air pollution monitoring<br />#2 issue: Limited role of citizens in pollution management<br /><ul><li> Urban pollution = anthropogenic effect
    15. 15. No real citizen participation despite international agreements</li></ul>“Environmental issues are best handled with the participation of all concerned citizens..” [Principle 10, Rio Declaration, 1992]<br />Needing to involve the public in the debate :<br /> to get a better representation of their environmental conditions<br /> To interact in a more direct and powerful way<br />
    16. 16. What if every mobile device <br />had an noise (air) sensor? <br />NoiseTube Project: Newgreen user experience<br /><ul><li> Phone = lowcostmeasurementdevice
    17. 17. Personalizedenvironmental information (healthdevice) </li></ul>Issue #2 - Social/political sciences <br /> Citizen empowerment<br /><ul><li> Citizens in the loop: reporting directly their environmental conditions
    18. 18. Building collective maps of their shared exposure to noise </li></ul>Issue #1 - Environmental/ health Sciences<br /> Supplying real exposure data<br /><ul><li> Low cost adaptive sensor network
    19. 19. Collecting fine-grained real data</li></li></ul><li>Why now?Opportunity of P.S. in environmental context<br />+<br />+<br /> Democratization of powerful & rich-sensor phones<br />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<br />Growing public concern<br />Cultural shift in digital world (Web 2.0)<br /><ul><li>Autonomy/freedom (no need to wait official/expert)
    20. 20. New opportunities for public discourse</li></li></ul><li>How does it works?<br />
    21. 21. Challenge 1: accuracy<br />Signal processing algorithm to compute Leq(A)<br />+<br />+<br />Phone in hand <br />Handsfree kit <br />Phone in pocket<br />Phone as noise sensor<br />Leq<br />A-weighted filter <br />Phone specific correction function<br />± 6.5 dB <br />± 2.5 dB <br />± 4.5 dB <br />Experiments to evaluate accuracy<br />
    22. 22. Challenge 2: Contextualizing environmental data<br />Why do we need the context? add meaning to raw data <br />1- Hard to search in numericaldatasets for humans<br /> Meaning of 75 dB(A): bad /good? Lat,Lng={2.34, 12.5}: which street?<br />2- Hard to identify the source of pollution with only numerical data <br />Only measurements, No semantic information <br />Measurement done by real sensors<br />Simulated map<br />
    23. 23. Challenge 2: Contextualizing environmental data<br /> New tagging usage: People as semantic sensors for pollution <br />Great but limited (amount of) contextual information<br />
    24. 24. Challenge 2: Contextualizing environmental data<br /> Machine tagging: Enriching the context with classifiers<br />Roadwork<br />Neighbors<br />Loudness <br />Noise Exposure<br />Signal Pattern<br />Location type<br />Location<br />Street name<br />City Name<br />Day<br />Week <br />Time<br />Season<br />Type<br />Winds<br />Weather<br />Temperature<br />Mobility<br />User Activity<br />
    25. 25. Challenge 3: visualisation <br /><ul><li>Exposure layer
    26. 26. Semantic layer
    27. 27. Contextual information
    28. 28. Contribution layer </li></ul>Google Earth<br />+ Web-based<br />Real-time collective exposure <br />
    29. 29. Challenge 4: Sharing<br />Connected to the people <br /><ul><li>ELog: Environmental log “See the digital traces of my exposure to pollution“
    30. 30. New Grid for personal environmental information: Sprending environmental information through Social Network (Twitter) </li></ul>Widget on blog<br />
    31. 31. Citizens empowerment<br />Case study: Exposure to noise in mass transit system<br />“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)<br />Paris Subway - 2008<br /><ul><li>No public information about exposure to noise
    32. 32. Building exposure map of 2 lines</li></li></ul><li>Conclusion<br /> NoiseTube: Participatory model to monitor noise pollution using mobile phones <br />Newgreen user experience<br />“Elog” (Exposure log): Reporting and sharing personal exposure to the community<br />Low cost adaptive sensor network supplying real exposure data<br />Future work <br /><ul><li>Experimentation: BruitParif, open Lab , Brussels, India, Italy)
    33. 33. Data quality of peer production system in the physical world
    34. 34. Injectingsemantics to transform large raw data intoactionableknowledge
    35. 35. Mechanism to support cooperation / collective action</li>