Jongwon Yoon 2011. 03. 28

1. Providing User Context for Mobile andSocial Networking ApplicationsA. C. Santos et al., Pervasive and Mobile Computing, vol. 6, no. 1, pp. 324-341, 2010. Jongwon Yoon 2011. 03. 28

2. 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, …

3. Sensors and Prototype • System • Sony Ericsson W910i mobile phone or Nokia N95 mobile phone • BlueSentry external sensor node: Communicates with the smartphone 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

4. System Architecture

5. 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

6. 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

7. Sensor Data Acquisition (cont.)

8. Preprocessing and Feature Extraction

9. Context Inference • Four contexts • Walking, Running, Resting, Idle • Decision tree-based inference • ID3 algorithm

10. 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

11. 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 aggregated 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

12. Context Publication: Experiments

13. System performance

14. 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

15. 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

16. Discussion Points • Data preprocessing and context inference method • Usage of published contexts • Possible services and applications with inferred contexts • System performance & battery issues