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
Slides from http://nslab.ee.ntu.edu.tw/NetworkSeminar/slides/cenceme.ppt
Outline
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Introduction of CenceMe
Design Consideration
CenceMe Implementation
CenceMe Classifier
System Performance
User Study
Conclusion
Motivation
• Text messaging: “Where are you?” “What are
you doing?”
• 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
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Design, implementation and evaluation
Lightweight classifier
Trade-off: time fidelity v.s. latency
Complete User study
Mobile Phone Limitations
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OS Limitations
API and Operational Limitations
Security Limitations
Energy Management Limitations
CenceMe Architecture
Architecture Design Issues
• Split-Level Classification (primitives, facts)
– Customized tag
– Resiliency
– Minimize bandwidth usage/energy
– Privacy/data integrity
• Power Aware Duty-Cycle
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
Human voice
Environment noise
Mean
Standard Deviation
Phone classifiers (2/2)
Sitting
• Activity
Standing
Walking
Running
Time
Backend Classifier
• Conversation
• Social context
– Neighborhood conditions
– Social status
• Mobility mode detector (vehicle or not)
• Location (to description/icon)
• “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 Results
Phone Placement on Body
• Pocket, lanyard, clipped to belt
• Insignificant impact conversation vs. Non-conversation
Environmental Impacts
• Independent of activity classification
• More important: transition between locations
Duty Cycle
• 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:
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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
User Study
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A new way to connect people
What is the potential CenceMe demographic?
Learn about yourself and your friends
My friends always with me
Conclusion
• A complete design, implementation and evaluation
• First application to retrieve and publish sensing
presence
• A complete user study and feedback for future
improvement