1. Urban Sensing Systems: Opportunistic or Participatory?
Nicholas D. Lane
Shane B. Eisenman
Mirco Musolesi
Emiliano Miluzzo
Andrew T. Campbell
Presented By:
Pradeep Vaghela

2. Outline
Introduction
Evaluation Model
Related Work
Conclusion

3. Introduction
Urban Sensing Systems: large scale systems to sense the environment through making use of static or mobile sensors
“People Centric Sensing” – Using mobility of people and their mobile devices

4. Introduction
Reference:Mesh Networking , The rise of people centric Sensing- MetroSense Group

5. Introduction
Metrosence - A new wireless sensor edge network based on the concept of "people-centric sensing" at a large scale (campus, town, metropolies)
Various projects: CenseMe-facilitate the sharing of "presence" information among friends.
AnonySense project, we are working to ensure the security, trust, and privacy of people-centric data

6. Architecture of People centric sensing system
Query submission-sensor type and sensing condition
Device selection- which resources to fulfill the query mobile phones or Wi-Fi access points
Sensor sampling- when to sample
Data analysis, sharing presentation

7. Introduction Custodians of the mobile devices play vital role
Participatory- active owner
Opportunistic- passive owner

8. Introduction- Participatory Sensing
Custodian involved at significant decision stages such as – what data to share, privacy mechanism
Example: DietSense( CENS-UCLA)

9. Introduction- Opportunistic Sensing
Device is utilized whenever application requirements are met
Privacy and transparency are importance issues
Example : Pothole Petrol
Participatory or Opportunistic ?

10. An evaluation model
Quantitative evaluation of the choice of the participatory model against opportunistic one
Notation. M – total no of devices in the system
Paccept_query
Puser_adapt
Pcontext_match
Phave_sensor

11. An evaluation model Opportunist is better when,
Participatory Success Probability,
Opportunistic Success Probability,
Opportunist is better when,

12. An evaluation model
Quantitative evaluation through Google street view like application
Phave_sensor = 0.8( 80% of the cellphones have camera)
Pcontext_match = 838/(365*24*60)
Pquery_accept = 0.3( contact rate using first call attempts)
Puser_adapt = 0.2 ( again using contact rate)

13. An evaluation model..

14. An evaluation model..

15. An evaluation model..
Opportunistic good when constituent model probabilities are in favor. Also performance degrade slowly
Increasing the number of devices only amplifies the positive aspect of the opportunistic approach
Therefore, given uncertainty in the user population size and user participation probability supporting opportunistic sensing is well motivated.

16. Related Work Most system represent a middle ground
Opportunistic- Cartel
Urban Sensing project CENS demands more human intervention in the sensing loop. In DietSense , user takes photograph of the food

17. Conclusion
Two potential roles of sensor custodians in urban sensing systems are compared.
Opportunistic sensing design more easily supports large scale deployments and application diversity.

18. Critique and Questions?
How reliable probabilities are ?