Transcript [Tamer Nadeem]

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
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
Introduction of CenceMe
• People-centric sensing application
• Implementation on Nokia N95;
Symbian/JME VM platform
• Share user presence information
(Facebook)
Contributions
• Design, implementation and evaluation
• Lightweight classifier
• Trade-off: time fidelity v.s. latency
• Complete User study
Mobile Phone limitations
• OS Limitations
• API and Operational Limitations
• Security Limitations
• Energy Management Limitations
Architecture Design Issues
• Split-Level Classification (primitives, facts)
– Customized tag
– Resiliency
– Minimize bandwidth usage/energy
– Privacy/data integrity
• Power Aware Duty-Cycle (~6 hours)
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 (!!!)
Phone Software
ClickStatus
Backend Software
Phone classifiers (1/2)
• Audio
– Feature extraction
– Classification
Phone classifiers (2/2)
• Activity
Backend Classifier
• Conversation
• Social Context
– Neighborhood conditions
– Social Status
• Mobility Mode Detector
• Location
• “Am I Hot”
– Nerdy, party animal, cultured, healthy, greeny
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
General Result
Phone placement on body
• Pocket, lanyard, clipped to belt
• Insignificant impact conversation v.s. Non-conversation
Environmental impact
• Independent of activity classification
• More important: transition between locations
Duty Cycle (1/2)
• Problem detecting short term event
• Experiment: 8 people. Reprogram different duty cycles.
Power Benchmarks
• Measuring battery voltage, current, temperature
• Battery lifetime: 6.22+/- 0.59 hours
Memory and CPU Benchmarks
User Study
• Survey user experience
• Feedback:
– Positive from all users
– Willing to share detail status and presence information on
Facebook
– Privacy not an issue
– Stimulate curiosity among users
– Self-learning on activity patterns and social status
Rooms for improvement
• Battery life to up to 48 hours
• Finer grained privacy policy settings.
• Shorter classification time
Conclusion
• A complete design, implementation and evaluation
• First application to retrieve and publish sensing presence
• A complete user study and feedback for future
improvement