Design, Implementation and Evaluation of CenceMe Application COSC7388 – Advanced Distributed Computing Presentation By Sushil Joshi

Outline Introduction Architectural Design Limitations Split level classification Architectural Diagram Classifier Phone Classifier Backend Classifier Performance Power and Memory Benchmark Experimental Deployment and feedback

Introduction Mobile application that infers personal presence and updates the status to social networks. Sensor devices like microphone, accelerometer, GPS, camera and bluetooth inbuilt in Nokia N95. An always-on application needs to use energy in as efficient way as possible.

Introduction SenseLearnShare Information and process flow in CenseMe System

Introduction Realizing vision of automatic updates to social networks. Enablers – Integration of sensors to consumer mobile devices. Vision about bluetooth enabled cellphone talking to Other devices attached in running shoes, BlueCell dongle Attached to other user Sensor available in town ecosystem like carbon- dioxide or pollen sensors.

Nokia N800, N95, Nokia 5500, Tmote Mini, BlueCell Dongle.

Architectural Design (Limitations) Symbian OS Exception handlers API limitations – e.g. Missing JME API to access N95 internal accelerometer Security Limitations Energy Management Limitations

Architectural Design (Split level Classification)

Architectural Design (Split Level Classification) Advantages Minimizes sensor data that needs to be uploaded Resiliency when Radio/WiFi dropout by buffering and batching primitives Minimizes sensor data that needs to be uploaded thus saving energy that would be used up.

Architectural Diagram (Phone Software)

Architectural Diagram (Backend)

Classifier (Phone Classifier) DFT of human voice sample registered by Nokia N95 microphone DFT of audio sample from noisy environment as registered by Nokia N95 microphone

Classifier (Phone Classifier) Discriminant analysis clustering which determines the dashed lines (threshold between talking and non-talking)

Classifier (Phone Classifier) Data collected by Nokia N95 on-board accelerometer for different activities like sitting and walking.

Classifier (Backend Classifier) Rolling window of size N=5 used by conversation classifier Assymetric strategy P1P2P3P4P5 p1p2p3p4p5 Conversation No Conversation Primitive indicates voice Primitive indicates no voice

Classifier (Backend Classifier) Social Context classifier Mobility Mode Detector Location Classifier Historical trend of user data to identify behaviorial pattern. e.g. Nerdy, party animal, health conscious.

Performance Table 2 indicates false positives which could be attributed to either sensors grasping human voice from background or due to assymetric strategy for conversation classification.

Performance Conversation classifier accuracy in different ambience

Performance Conversation Classifier accuracy with varying duty cycle

Performance Accuracy of activity classification vs different positioning of mobile phone

Power, Memory and CPU Usages Power consumption during sampling/upload interval

Power, Memory and CPU Usages Screen saver mode turned on while using Nokia Energy Profiler so as to decouple energy used to light up the LCD screen.

Feedback From Experimental Deployment More likely to be used by population who already use social networking. Far less deletion of random images compared to uploads. Location feature mostly used. Can reveal lifestyle trends e.g less physical activity

Questions ?

Reference [1]Miluzzo, Emiliano, Lane, Nicholas D., Fodor, Krist\'of, sPeterson, Ronald, Lu, Hong, Musolesi, Mirco, Eisenman, Shane B., Zheng, Xiao, Campbell, Andrew T., Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application, SenSys '08: Proceedings of the 6th ACM conference on Embedded network sensor systems, pp , ACM, New York, NY, USA, [2] Emiliano Miluzzo, Nicholas D. Lane, Shane B. Eisenman, and Andrew T. Campbell, CenceMe – Injecting Sensing Presence into Social Networking Applications