Mobile Sensing: Leveraging Mobile Phones to Support Personal, Community, and Participatory Sensing Nithya Ramanathan Colla...
Text Entry Imagers Audio Location (GPS) Accelerometer Bluetooth Network Connectivity What can one person do with this powe...
Reddy, Samanta, Burke, Estrin, Hansen, Srivastava Presentation Presentation Visualization Processing Raw Data Can real-tim...
Mobile Sensing Grassroots data collection <ul><li>Scalable </li></ul><ul><li>Affordable </li></ul><ul><li>Believable </li>...
Mobile Sensing Grassroots data collection <ul><li>Scalable </li></ul><ul><li>Affordable </li></ul><ul><li>Believable </li>...
Mobile Sensing Grassroots data collection <ul><li>Scalable  Affordable  Believable </li></ul>Reddy, Samanta, Burke, Estrin...
Data Campaigns Reddy, Samanta, Burke, Estrin, Hansen, Srivastava Phones enhance participation Make it.. Easier to collect ...
Data Campaigns Reddy, Samanta, Burke, Estrin, Hansen, Srivastava Phones enhance participation Make it.. Easier to collect ...
Image and activity data to study pollution exposure http://www-ramanathan.ucsd.edu/ProjectSurya.html In collaboration with...
Active Image Collection for citizen science http://www.windows.ucar.edu/citizen_science/budburst In collaboration with the...
Reflecting on and learning from personal mobility Cyclesense  combines location data and users’ photos to give bikers dail...
Audio and location collection to recall family interactions http://urban.cens.ucla.edu/projects/familydynamics/ http://www...
While off-the-shelf components can be used to implement data campaigns, there is still room for improvement….
Protecting privacy while still collecting meaningful data <ul><li>Example: Images of food contain compromising objects in ...
Design to Maximize Trust and Participation Design Theme :   Involve rather than burden the user by designing    systems th...
http://urban.cens.ucla.edu/
PEIR Press Release
Passive Image Collection for Diet Recall Studies http://urban.cens.ucla.edu/projects/dietsense/ In collaboration with the ...
Data Analysis and Using external data streams  Example: Estimating Pollution without Pollution Sensors Lifelong damage fou...
Urban Sensing:  Research Challenges <ul><li>Scaling and credibility. </li></ul><ul><li>Coordinated, opportunistic sampling...
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4 Environmental Sustainability Ws Nithya Ramanathan

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  • 4 Environmental Sustainability Ws Nithya Ramanathan

    1. 1. Mobile Sensing: Leveraging Mobile Phones to Support Personal, Community, and Participatory Sensing Nithya Ramanathan Collaborating Faculty: Jeff Burke, Deborah Estrin, Mark Hansen, Mani Srivastava, Ruth West UCLA Center for Embedded Networked Sensing UCLA Center for Research in Engineering, Media and Performance Staff and Graduate Students: Faisal Alquaddoomi, Betta Dawson, Jeff Goldman, Eric Howard, August Joki, Donnie Kim, Vinayak Naik, Min Mun, Nicolai Petersen, Sasank Reddy, Jason Ryder, Vids Samanta, Katie Shilton, Nathan Yau UCLA Departments of Computer Science, Electrical Engineering, Statistics CENS Urban Sensing collaborators also include: Mark Allman, Dana Cuff, Jerry Kang, Vern Paxson, Fabian Wagmister, CENS Urban Sensing funding sources include: NSF CRI, NeTS-FIND, and OIA; Cisco, Nokia, Schematic, Sun, Walt Disney Imagineering R&D http://urban.cens.ucla.edu
    2. 2. Text Entry Imagers Audio Location (GPS) Accelerometer Bluetooth Network Connectivity What can one person do with this powerful tool?
    3. 3. Reddy, Samanta, Burke, Estrin, Hansen, Srivastava Presentation Presentation Visualization Processing Raw Data Can real-time feedback about our actions change our behavior?
    4. 4. Mobile Sensing Grassroots data collection <ul><li>Scalable </li></ul><ul><li>Affordable </li></ul><ul><li>Believable </li></ul>Reddy, Samanta, Burke, Estrin, Hansen, Srivastava What can thousands of coordinated people do with this powerful tool?
    5. 5. Mobile Sensing Grassroots data collection <ul><li>Scalable </li></ul><ul><li>Affordable </li></ul><ul><li>Believable </li></ul>Reddy, Samanta, Burke, Estrin, Hansen, Srivastava What can thousands of coordinated people do with this powerful tool?
    6. 6. Mobile Sensing Grassroots data collection <ul><li>Scalable Affordable Believable </li></ul>Reddy, Samanta, Burke, Estrin, Hansen, Srivastava What can thousands of coordinated people do with this powerful tool?
    7. 7. Data Campaigns Reddy, Samanta, Burke, Estrin, Hansen, Srivastava Phones enhance participation Make it.. Easier to collect more data Data is more credible and verifiable
    8. 8. Data Campaigns Reddy, Samanta, Burke, Estrin, Hansen, Srivastava Phones enhance participation Make it.. Easier to collect more data Data is more credible and verifiable No Technological Innovation
    9. 9. Image and activity data to study pollution exposure http://www-ramanathan.ucsd.edu/ProjectSurya.html In collaboration with UCSD, SRU and TERI in India Characterize Outdoor Activities Infer Duration of Exposure Collect Indoor Pollution Levels Bluetooth temperature sensor Phone + GPS, accelerometer Special soot filter
    10. 10. Active Image Collection for citizen science http://www.windows.ucar.edu/citizen_science/budburst In collaboration with the ongoing citizen science initiative known as BudBurst
    11. 11. Reflecting on and learning from personal mobility Cyclesense combines location data and users’ photos to give bikers daily feedback and suggestions on the quality and safety of their commutes. In PEIR , the combination of location, time, and activity are automatically interpreted using regional air quality models to estimate participants’ exposure to particulate matter. http://peir.cens.ucla.edu/ http://urban.cens.ucla.edu/projects/cyclesense/
    12. 12. Audio and location collection to recall family interactions http://urban.cens.ucla.edu/projects/familydynamics/ http://www.kt.tu-cottbus.de/speech-analysis/ http://urban.cens.ucla.edu/projects/familydynamics/ In collaboration with the Semel Institute
    13. 13. While off-the-shelf components can be used to implement data campaigns, there is still room for improvement….
    14. 14. Protecting privacy while still collecting meaningful data <ul><li>Example: Images of food contain compromising objects in the background </li></ul><ul><li>Ways to approach the problem </li></ul><ul><li>Just don’t upload the sensitive data in the first place. Analysis on phone and partial sampling (FamilyDynamics) </li></ul><ul><li>Give the user control over which data to release. Selective sharing (DietSense) </li></ul><ul><li>Release the data, but add noise or obfuscate certain portions as needed. Location cloacking (PEIR) </li></ul><ul><li>Only release aggregates of the data across some window in time or space Data aggregates (PEIR) </li></ul>
    15. 15. Design to Maximize Trust and Participation Design Theme : Involve rather than burden the user by designing systems that are easy to use and understand. Design Theme : Validate data.
    16. 16. http://urban.cens.ucla.edu/
    17. 17. PEIR Press Release
    18. 18. Passive Image Collection for Diet Recall Studies http://urban.cens.ucla.edu/projects/dietsense/ In collaboration with the public health department at UCLA
    19. 19. Data Analysis and Using external data streams Example: Estimating Pollution without Pollution Sensors Lifelong damage found in 13-year study of 3,600 Southland youngsters living within 500 yards of a highway. The Los Angeles Times, 1/26/07 Houston, Winer et al Source: McConnell et al. Traffic, Susceptibility, and Childhood Asthma. Environ Health Perspect 114:766–772 (2006)
    20. 20. Urban Sensing: Research Challenges <ul><li>Scaling and credibility. </li></ul><ul><li>Coordinated, opportunistic sampling. </li></ul><ul><li>Network attestation and verification of location, time, and other context. </li></ul><ul><li>Encouraging sharing. </li></ul><ul><li>Data protection and selective, resolution-controlled dissemination. </li></ul><ul><li>Participatory privacy </li></ul><ul><li>Anonymous and pseudonymous participation. Reputation, incentive, and authoring frameworks. </li></ul><ul><li>Finding, visualizing, and analyzing data. </li></ul><ul><li>Data stream naming, privacy-respecting discovery, and signal search. </li></ul><ul><li>Server-side signal processing for data processing, browsing, and auditing. </li></ul><ul><li>Spatial interfaces to data and authoring. </li></ul><ul><li>Infrastructure for capture, review, processing. </li></ul><ul><li>Adaptive collection protocols. </li></ul><ul><li>Automatic feeding of data to models. </li></ul>Privacy isn’t a separate concern... it’s embedded in the sensing and research activities... it has variable meaning in specific circumstances and settings... it will skew participation and data collection

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