Abstract
The ability to predict the future contexts of users significantly improves service quality and user satisfaction in ubiquitous computing environments. Location prediction is particularly useful because ubiquitous computing environments can dynamically adapt their behaviors according to a user’s future location. In this paper, we present an inductive approach to recognizing a user’s location by establishing a dynamic Bayesian network model. The dynamic Bayesian network model has been evaluated with a set of contextual data collected from undergraduate students. The evaluation result suggests that a dynamic Bayesian network model offers significant predictive power.
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References
Anagnostopoulos, T., Anagnostopoulos, C., Hadjiefthymiades, S., Kyriakakos, M., Kalousis, A.: Predicting the Location of Mobile Users: a Machine Learning Approach. In: The ACM International Conference on Pervasive Services, pp. 65–72 (2009)
Brdiczka, O., Reignier, P., Crowley, J.: Detecting Individual Activities from Video in a Smart Home. In: 11th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, pp. 363–370 (2007)
Davison, B.D., Hirsh, H.: Predicting Sequences of User Actions. In: AAAI/ICML Workshop on Predicting the Future: AI Approaches to Time–Series Analysis, pp. 5–12 (1998)
Feng, Y., Teng, T., Tan, A.: Modeling Situation Awareness for Context-aware Decision Support. Expert Systems with Applications 36(1), 455–463 (2009)
Hong, J., Suh, E., Kim, S.: Context-aware Systems: A Literature Review and Classification. Expert Systems with Applications 36(4), 8509–8522 (2008)
Hu, M.: Model Checking for Incomplete High Dimensional Categorical Data. Ph.D. Dissertation, University of California, Los Angeles, Los Angeles, CA (1999)
Huýnh, T., Fritz, M., Schiele, B.: Discovery of Activity Patterns using Topic Models. In: 10th International Conference on Ubiquitous Computing, pp. 10–19 (2008)
Hwang, K., Cho, S.: Landmark Detection from Mobile Life Log using a Modular Bayesian Network Model. Expert Systems with Applications 36(10), 12065–12076 (2009)
Kim, E., Helal, S., Cook, D.: Human Activity Recognition and Pattern Discovery. IEEE Pervasive Computing 9(1), 48–53 (2010)
Laasonen, K., Raento, M., Toivonen, H.: Adaptive On-device Location Recognition. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 287–304. Springer, Heidelberg (2004)
Murphy, K.: Dynamic Bayesian Networks: Representation, Inference and Learning. Ph.D. Dissertation, University of California, Berkeley, Berkeley, CA (2002)
Pearl, J.: Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge (2000)
Perl, J.: A Neural Network Approach to Movement Pattern Analysis. Human Movement Science 23(5), 605–620 (2004)
Petzold, J., Pietzowski, A., Bagci, F., Trumler, W., Ungerer, T.: Prediction of Indoor Movements using Bayesian Networks. In: Strang, T., Linnhoff-Popien, C. (eds.) LoCA 2005. LNCS, vol. 3479, pp. 211–222. Springer, Heidelberg (2005)
Rashidi, P., Cook, D., Holder, L., Schmitter-Edecombe, M.: Discovering Activities to Recognize and Track in a Smart Environment. IEEE Transactions on Knowledge and Data Engineering (2010) (in press)
Singla, G., Cook, D., Schmitter-Edgecombe, M.: Recognizing Independent and Joint Activities among Multiple Resident in Smart Environments. Ambient Intelligence and Humanized Computing Journal 1(1), 57–63 (2010)
Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufman, San Francisco (2005)
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Lee, S., Lee, K.C., Cho, H. (2010). A Dynamic Bayesian Network Approach to Location Prediction in Ubiquitous Computing Environments. In: Papasratorn, B., Lavangnananda, K., Chutimaskul, W., Vanijja, V. (eds) Advances in Information Technology. IAIT 2010. Communications in Computer and Information Science, vol 114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16699-0_9
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DOI: https://doi.org/10.1007/978-3-642-16699-0_9
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