CenceMe - Interactive Computing Lab

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 • • • • • • •

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 • • • •

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

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: – – – – –

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 • • • •

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