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