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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

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slide1

Design, Implementation and Evaluation of CenceMe Application

  • COSC7388 – Advanced Distributed Computing
  • Presentation By
  • Sushil Joshi
slide2

Outline

  • Introduction
  • Architectural Design
    • Limitations
    • Split level classification
    • Architectural Diagram
  • Classifier
    • Phone Classifier
    • Backend Classifier
  • Performance
  • Power and Memory Benchmark
  • Experimental Deployment and feedback
slide3

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.

slide4

Introduction

Sense

Learn

Share

Information and process flow in CenseMe System

slide5

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.
slide8

Architectural Design (Limitations)

Symbian OS Exception handlers

API limitations – e.g. Missing JME API to access N95 internal accelerometer

Security Limitations

Energy Management Limitations

slide10

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.
slide13

Classifier (Phone Classifier)

DFT of audio sample from noisy environment as registered by Nokia N95 microphone

DFT of human voice sample registered by Nokia N95 microphone

slide14

Classifier (Phone Classifier)

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

slide15

Classifier (Phone Classifier)

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

slide16

Classifier (Backend Classifier)

  • Rolling window of size N=5 used by conversation classifier
  • Assymetric strategy

P1

P2

P3

P4

P5

Conversation

p1

p2

p3

p4

p5

No Conversation

Primitive indicates voice

Primitive indicates no voice

slide17

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.

slide18

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.

slide19

Performance

Conversation classifier accuracy in different ambience

slide20

Performance

Conversation Classifier accuracy with varying duty cycle

slide21

Performance

Accuracy of activity classification vs different positioning of mobile phone

slide22

Power, Memory and CPU Usages

Power consumption during sampling/upload interval

slide23

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.

slide24

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

slide26

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