D u k e S y s t e m s 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: Text messaging: “Where R U?” “Where R U?” “What R U doing?” “What R U doing?” Mobile phones are virtually always ON and with us Mobile phones are virtually always ON and with us Sensors in mobile phone: GPS, accelerometers, microphone, camera … etc Sensors in mobile phone: GPS, accelerometers, microphone, camera … etc Data collection through sensors Data collection through sensors

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

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

Mobile Phone limitations OS Limitations OS Limitations API and Operational Limitations API and Operational Limitations Security Limitations Security Limitations Energy Management Limitations Energy Management Limitations

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

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

Phone Software

ClickStatus

Backend Software

Phone classifiers (1/2)‏ Audio Audio –Feature extraction –Classification

Phone classifiers (2/2)‏ Activity Activity

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

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

General Result

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

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

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

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

Power Benchmarks Measuring battery voltage, current, temperature Measuring battery voltage, current, temperature Battery lifetime: 6.22+/ hours Battery lifetime: 6.22+/ hours

Memory and CPU Benchmarks

User Study Survey user experience Survey user experience Feedback: 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

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

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

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