Sensing Meets Mobile Social Networks: The Design, Implementation and Evaluation of the CenceMe Application

1. Sensing Meets Mobile Social Networks: The Design, Implementation and Evaluation of the CenceMe Application Emiliano Miluzzo†, Nicholas D. Lane†, Kristóf Fodor†, Ronald Peterson†, Hong Lu†, Mirco Musolesi†, Shane B. Eisenman§, Xiao Zheng†, Andrew T. Campbell† †Computer Science, Dartmouth College §Electrical Engineering, Columbia University Presented by Amre Shakimov CompSci 215

2. Motivation • Text messaging: • “Where R U?” • “What R U doing?” • Mobile phones are virtually always ON and with us • Sensors in mobile phone: GPS, accelerometers, microphone, camera … etc • Data collection through sensors

3. Introduction of CenceMe • People-centric sensing application • Implementation on Nokia N95; Symbian/JME VM platform • Share user presence information (Facebook)‏

4. Contributions • Design, implementation and evaluation • Lightweight classifier • Trade-off: time fidelity v.s. latency • Complete User study

5. Mobile Phone limitations • OS Limitations • API and Operational Limitations • Security Limitations • Energy Management Limitations

6. Architecture Design Issues • Split-Level Classification (primitives, facts)‏ • Customized tag (?)‏ • Resiliency • Minimize bandwidth usage/energy • Privacy/data integrity • Power Aware Duty-Cycle (~6 hours)‏

7. CenceMe Implementation Operations (Phone): • Sensing • Classification to produce primitives • Presentation of people's presence on the phone • Upload of primitives to backend servers Classifications (Backend Server): • classifying the nature of the sound collected from the microphone • classifying the accelerometer data to determine activity (sitting, standing, walking, running) • scanned Bluetooth/MAC addresses in range • GPS readings • random photos (!!!)

8. Phone Software

9. ClickStatus

10. Backend Software

11. Phone classifiers (1/2)‏ • Audio • Feature extraction • Classification

13. Phone classifiers (2/2)‏ • Activity

14. Backend Classifier • Conversation • Social Context • Neighborhood conditions • Social Status • Mobility Mode Detector • Location • “Am I Hot” (???)‏ • Nerdy, party animal, cultured, healthy, greeny

15. System Performance • Classifier accuracy • Impact of mobile phone placement on body • 8 users • Annotations as ground truth for comparison with classifier outputs • Environmental conditions • Sensing duty cycles

16. General Result

17. Phone placement on body • Pocket, lanyard, clipped to belt • Insignificant impact conversation v.s. Non-conversation

18. Environmental impact • Independent of activity classification • More important: transition between locations

19. Duty Cycle (1/2)‏ • Problem detecting short term event • Experiment: 8 people. Reprogram different duty cycles.

20. Duty Cycle (2/2)‏ • N=5, Conversation classification delay: 1.5 mins • N=30, Conversation classification delay: 9 mins

21. Power Benchmarks • Measuring battery voltage, current, temperature • Battery lifetime: 6.22+/- 0.59 hours

22. Memory and CPU Benchmarks

23. User Study • Survey user experience • Feedback: • Positive from all users • Willing to share detail status and presence information on Facebook • Privacy not an issue (really?!)‏ • Stimulate curiosity among users • Self-learning on activity patterns and social status

24. Rooms for improvement • Battery life to up to 48 hours • Finer grained privacy policy settings. • Shorter classification time

25. Conclusion • A complete design, implementation and evaluation • First application to retrieve and publish sensing presence • A complete user study and feedback for future improvement

26. Pros&Cons • Pros • A complete (first?) user study • Use off-the-shelf devices • Cons • Non-pragmatic • Looks a little bit scarce (no solid story behind)‏ • Energy consumption problem