Post on 05-Jan-2016
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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
Sense Learn Share
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
P1 P2 P3 P4 P5
p1 p2 p3 p4 p5
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. 337--350, ACM, New York, NY, USA, 2008.
[2] Emiliano Miluzzo, Nicholas D. Lane, Shane B. Eisenman, and Andrew T. Campbell, CenceMe – Injecting Sensing Presence into Social Networking Applications