IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
권호 표지

Context Awareness Reasoning System for Personalized Services in Ubiquitous Mobile Environments

유비쿼터스 모바일 환경에서 개인화 서비스를 위한 상황인지 추론 시스템

  • 문애경 (한국전자통신연구원, 서비스융합연구팀) ;
  • 박유미 (한국전자통신연구원, 서비스융합연구팀) ;
  • 김상기 (한국전자통신연구원, 서비스융합연구팀) ;
  • 이병선 (한국전자통신연구원, 서비스융합연구팀)
  • Received : 2009.06.26
  • Accepted : 2009.08.14
  • Published : 2009.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposed the context awareness reasoning system to provide the personalized services dynamically in a ubiquitous mobile environments. The proposed system is designed to provide the personalized services to mobile users and consists of the context aggregator and the knowledge manager. The context aggregator can collect information from networks through Open API Gateway as well as sensors in a various ubiquitous environment. And it can also extract the place types through the geocoding and the social address domain ontology. The knowledge manager is the core component to provide the personalized services, and consists of activity reasoner, user pattern learner and service recommender to provide the services predict by extracting the optimized service from user situations. Activity reasoner uses the ontology reasoning and user pattern learner learns with previous service usage history and contexts. And to design service recommender easy to flexibly apply in dynamic environments, service recommender recommends service in the only use of current accessible contexts. Finally, we evaluate the learner and recommender of proposed system by simulation.

Keywords

References

  1. A. Moon, H. Kim, H. Kim and S. Lee, "Context-Aware Active Services in Ubiquitous Computing Environments," ETRI Journal vol. 29, no. 2, 2007, pp. 169-178. https://doi.org/10.4218/etrij.07.0106.0075
  2. 홍일영, "상황인지 소프트웨어, 이젠 몸짓을 넘어 마음을 읽어야 한다!," 한국 소프트웨어 진흥원, 정책연구센터, 2008.
  3. 박재영 외 4인, "국외 개인화 서비스 기술 동향," TTA 저널/정보통신표준화소식, 2008.
  4. http://context.media.mit.edu/press/.
  5. S. McBurney, M. Williams, N. Taylor and E. Papadopoulou, "Managing User Preference for Personalization in a Pervasive Service Environment," IEEE Advanced International Conf. on Telecommunications, 2007.
  6. e-Sense, "Capturing Ambient Intelligence for Mobile Communications through Wireless Sensor Networks," IST, 2006.
  7. IST-2004-511607 MobiLife D27b (D4.1b) v1.0, 2004.
  8. SPICE, Deliverable $N^{\circ}$: 2, "Title: Specification of pro-active Service Infrastructure for Attentive Services," 2007.
  9. M. Sutter, O. Droegehorn and K. David, "User Profile Management on Service Platforms for Ubiquitous Computing Environment," IEEE Conf. Vehicular Technology, 2007 pp. 287-291.
  10. A. Krause, A. Smailagic and D. Siewiorek, "Context- Aware Mobile Computing: Learning Context-Dependent personal Preferences from a Wearable Sensor Array," IEEE Trans. on Mobile Computing, 5(2), 2006, pp. 113-127. https://doi.org/10.1109/TMC.2006.18
  11. M. Feki, S. Lee, Z. Bien and M. Mokhtai, "Context A ware Life Pattern Prediction Using Fuzzy-State Q-Learning," LNCS4541, 2007, pp.185-195.
  12. N. Golovin and E. Rahm, "Reinforcement Learning Architecture for Web Recommendations," Conf, in Information Technology: Coding and Computing, 2004.
  13. F. Herndex, E. Gaudioso and J. Boticario, "A Reinforcement Learning Approach to Achieve Unobtrusive and Interactive Recommendation Systems for Web-Based Communities," LNCS3137, 2004, pp. 409-412.
  14. P. Rojanavasu, P. Srinil and O. Pinngern, "New Recommendation System Using Reinforcement Learning," eBusiness, 2005.
  15. Henning Schulzrinne, Vijay Gurbani, Paul Kyzivat, and Jonathan Rosenberg, "RPID: Rich Presence Extensions to the Presence Information Data Format (PIDF)," IETF RFC 4480, July 2006.
  16. L. Kaelbling, M. Littman and A. Moore, "Reinforcement Learning: A Survey," Journal of Artificial Intelligence Research, vol. 4, 1996, pp. 237-285.
  17. S. Nguyen and A. Skowron, "Quantization of Real Value Attributes," Joint Annual Conf. Information on Sciences, 1995, pp. 34-375.
  18. K.Jearanaitanakij and O. Pinngern, "An Information Gain Technique for Acceleration of Convergence of Artificial Neural Networks," ICICS, 2005, pp. 349-352.
  19. http://archive.ics.uci.edu/ml/.
  20. S. Louis, A. Shankar, "Context Learning Can Improve User Interaction, Information Reuse and Integration," IEEE conf. IRI 2004, pp. 115-120.
  21. http://www.cs.waikato.ac.nz/ml/weka.