Providing User Context for Mobile and Social Networking Applications A. C. Santos et al., Pervasive...

Providing User Context for Mobile andSocial Networking Applications

A. C. Santos et al., Pervasive and Mobile Computing, vol. 6, no. 1, pp. 324-341, 2010.

Jongwon Yoon2011. 03. 28

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Introduction

• Importance of contexts for mobile value-added services– Some services must be enabled or disabled depending on the

user context– Can be used for Anti-theft or near-emergency services

• Requirement of mobile context-aware services– Mobile devices must be able to identify specific user contexts

• Data processing, accurate context inference, computing power, …

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Sensors and Prototype

• System– Sony Ericsson W910i mobile phone or Nokia N95 mobile phone– BlueSentry external sensor node: Communicates with the smart-

phone via bluetooth

• Sensors– Accelerometers, light, sound, humidity, temperature and GPS

sensors– Virtual sensors

• To acquire information such as the time of day and calendar events

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System Architecture

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Application

• Allowing different modes– Possibility of editing existing contexts– Continuous context-learning mode

• Provide different sensor readings and the identified contexts– Confidence value calculated as the percentage

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Sensor Data Acquisition

• Use API for sensor data– JSR-256 Mobile Sensor API– Provides developers with a standard way to retrieve data

• Same acquisition rate for all sensors– Except for the internal accelerometer: At twice the rate of the

other sensors

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Sensor Data Acquisition (cont.)

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Preprocessing and Feature Extraction

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Context Inference

• Four contexts– Walking, Running, Resting, Idle

• Decision tree-based inference– ID3 algorithm

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Context Inference: Experiments

• Divide examples into a training set and a testing set– Training set : 300 x 4 = 1200 examples– Test set : 200 x 4 = 800 examples

• Comparison method : C4.5

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Context Publication

• Advantages– Possible to enable, disable or change the behavior of value-

added services– Contexts can be augmented with information available at the

network level– Opens up the way to other services and applications

• Social networking, remote monitoring, health assistance, etc.– Provides the network operator with the ability to gather aggre-

gated data on multiple users to study different user profiles

• Analyzing data from multiple users– Cluster the sequences of context changes– Represented by a Markov chain : Transition probabilities

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Context Publication: Experiments

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System performance

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Application to Social Networking

• Roles of context information– Cope with user mobility– Update the current user status message with the current context– Enable actions associated with the current context

• online/offline mode, available/busy/away status– Tag content with the current context

• Applications– Twitter and Hi5– SAPO messenger

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Summary

• Context inference system– Layered architecture for the development of the system– Gathers information about user contexts– Prototype system: Inexpensive sensors + smartphone– Distinguishes between a number of daily activities

• Possibility of publishing the user context to an external server

– Enables a wide range of context-aware services– Example: Social networking websites

• Ongoing works– Different context inference approaches– Extending the experimental setup with additional sensors

• To accurately identify daily-life activities

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Discussion Points

• Data preprocessing and context inference method

• Usage of published contexts

• Possible services and applications with inferred contexts

• System performance & battery issues