Participatory Sensing

1. Participatory Sensing 4921013439 Huang, Ming-Chun

2. Outline • Motivation • Alternatives • Partisan Architecture • Other Applications and Campaigns

3. A Case • Asthma rates v.s Truck traffic density in New York City. • Year-Round Particle Pollution • What it is: Particle pollution refers to a mix of very tiny solid and liquid particles that are in the air we breathe. • Consequence: Asthma attacks, Lung cancer, and Cardiovascular disease

4. A Traditional Solution • Utilizing specialized equipments and manpower from community to document commercial truck traffic.

5. Result • Link truck traffic density with diesel exhaust particle pollution • Uncover illegal truck routes. • Data can influence public policy and health. But… It seems… problematic…

6. Take a second thought • Do the data accurate enough? • Are the people in the community objective enough? Even if hopefully everyone is careful, honest and neutral… But… this brutal force method takes too much money and too much time.

7. An Alternative: Participatory Sensing • Participatory Sensing: Let everyone be a debugger and integrate most of small piece of information. Enhance and Systematize those existing methodologies. Increase the quantity, quality and credibility of data with less cost and more convenience.

8. Suggested Techniques • Adaptive data collection protocols. • Geotagging with network-attested location and time -> Credibility Ask user to repeat and correct his observation before environment changes • Upload from where there is not yet network-connected • Save users’ time to concentrate on where there is insufficient coverage in dataset • Gather human activity patterns.

9. Grassroots (bottom-up) Benefits: • Low cost without waiting for a formal project or funding. • Let every citizen can be responsive to their environmental anomalies and examine expert assessments and judgments.

10. Partisan Architecture • Places users in the loop of the sensing process and aims to maximize the credibility of data they collect. • In situ measurement Core network service

11. In situ measurement • CENS : Center for Embedded Network Sensing • headquartered at UCLA • USC also participate in CENS-led research • In situ measurements Remote sensing. Require that the instrumentation be located directly at the point of interest and in contact with the subject of interest.

12. Core Network Service • What we are concerned about? ans: Network-level mechanisms Quality Checks & Privacy Control Context Verification & Resolution Control key: Mediator(Access Point & Router level)

13. Mediator’s Job • Location & Time • Phenomena of interest • Privacy

14. Network-Attested Context(location &time) • Credibility for decision-making By… • Tagging data packets RF Signal StrenghLocalizaiton & Timestamp

15. Physical context(phenomena of interest) • Directional microphone deployment. Ex: Orientation Ex: Team Localization • Averaging with reputation information.

16. Context Resolution Control (Privacy) • Follow user-defined/default privacy rule. • May need to deliberately hide the context info : Selective Sharing Concept • Add some random jitter to packets. • Routed through multi-mediator to hide network identifier: IP, host name.

17. Application and Campaign • Public health: Chronic and Environmental • Urban Planning: City or Park development • Cultural identity and creative expression Ubiquity of image capture with presence-based authentication. • Natural resource management

18. HumanModel • Initiator : Creator and Problem definer • Gatherer: Mobile User • Evaluator: Verify and Classify collected data. • Analyst : Process, Interpret, Present data and Give conclusions Future Goal: Distributed Data-Gathering

19. Conclusion Let participatory sensing become “Citizen Sensing” to uncover something was previous unobservable.

20. Reference • Participatory Sensing • Particle Pollution Description • Team Localization: A Maximum Likelihood Approach

21. Thanks For Your Attention Any Question???