Abstract
Context-awareness is viewed as one of the most important aspects in the emerging pervasive computing paradigm. Mobile context-aware applications are required to sense and react to changing environment conditions. Such applications, usually, need to recognize, classify and predict context in order to act efficiently, beforehand, for the benefit of the user. In this paper, we propose a mobility prediction model, which deals with context representation and location prediction of moving users. Machine Learning (ML) techniques are used for trajectory classification. Spatial and temporal on-line clustering is adopted. We rely on Adaptive Resonance Theory (ART) for location prediction. Location prediction is treated as a context classification problem. We introduce a novel classifier that applies a Hausdorff-like distance over the extracted trajectories handling location prediction. Since our approach is time-sensitive, the Hausdorff distance is considered more advantageous than a simple Euclidean norm. A learning method is presented and evaluated. We compare ART with Offline kMeans and Online kMeans algorithms. Our findings are very promising for the use of the proposed model in mobile context aware applications.
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References
Dey, A.: Understanding and using context. Personal and Ubiquitous Computing 5(1), 4–7 (2001)
Hightower, J., Borriello, G.: Location Systems for Ubiquitous Computing. IEEE Computer 34(8) (August 2001)
Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2004)
Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley-Interscience, Hoboken (2001)
Belogay, E., Cabrelli, C., Molter, U., Shonkwiler, R.: Calculating the Hausdorff Distance between Curves. Information Processing Letters 64(1), 17–22 (1997)
Choi, S., Shin, K.G.: Predictive and adaptive bandwidth reservation for hand-offs in QoS-sensitive cellular networks. In: ACM SIGCOMM (1998)
Hadjiefthymiades, S., Merakos, L.: Proxies+Path Prediction: Improving Web Service Provision in Wireless-Mobile Communications. ACM/Kluwer Mobile Networks and Applications, Special Issue on Mobile and Wireless Data Management 8(4) (2003)
Karmouch, A., Samaan, N.: A Mobility Prediction Architecture Based on Contextual Knowledge and Spatial Conceptual Maps. IEEE Trans. on Mobile Computing 4(6) (2005)
Viayan, R., Holtman, J.: A model for analyzing handoff algorithms. IEEE Trans. on Veh. Technol. 42(3) (August 1993)
Ashbrook, D., Starner, T.: Learning Significant Locations and Predicting User Movement with GPS. In: Proc. Sixth Int’l Symp. Wearable Computes (ISWC 2002), October 2002, pp. 101–108 (2002)
Priggouris, I., Zervas, E., Hadjiefthymiades, S.: Location Based Network Resource Management. In: Ibrahim, I.K. (ed.) Handbook of Research on Mobile Multimedia. Idea Group Inc. (May 2006)
Curewitz, K.M., Krishnan, P., Vitter, J.S.: Practical Prefetching via Data Compression. In: Proceedings of ACM SIGMOD, pp. 257–266 (1993)
Narendra, K., Thathachar, M.A.L.: Learning Automata – An Introduction. Prentice Hall, Englewood Cliffs (1989)
Cheng, Jain, R., van den Berg, E.: Location prediction algorithms for mobile wireless systems. In: Wireless Internet handbook: technologies, standards, and application, pp. 245–263. CRC Press, Boca Raton (2003)
Yavas, G., Katsaros, D., Ulusoy, O., Manolopoulos, Y.: A data mining approach for location prediction in mobile environments. Data and Knowledge Engineering 54(2) (2005)
Katsaros, D., Nanopoulos, A., Karakaya, M., Yavas, G., Ulusoy, O., Manolopoulos, Y.: Clustering Mobile Trajectories for Resource Allocation in Mobile Environments. In: Berthold, M.R., Lenz, H.-J., Bradley, E., Kruse, R., Borgelt, C. (eds.) IDA 2003. LNCS, vol. 2810, pp. 319–329. Springer, Heidelberg (2003)
Tao, Y., Faloutsos, C., Papadias, D., Liu, B.: Prediction and Indexing of Moving Objects with Unknown Motion Patterns. In: ACM SIGMOD (2004)
Nhan, V.T.H., Ryu, K.H.: Future Location Prediction of Moving Objects Based on Movement Rules. In: ICIC 2006. LNCIS, vol. 344, pp. 875–881. Springer, Heidelberg (2006)
Xiao, Y., Zhang, H., Wang, H.: Location Prediction for Tracking Moving Objects Based on Grey Theory. In: IEEE FSKD 2007 (2007)
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Anagnostopoulos, T., Anagnostopoulos, C., Hadjiefthymiades, S. (2010). An Online Adaptive Model for Location Prediction. In: Vasilakos, A.V., Beraldi, R., Friedman, R., Mamei, M. (eds) Autonomic Computing and Communications Systems. AUTONOMICS 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 23. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11482-3_5
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DOI: https://doi.org/10.1007/978-3-642-11482-3_5
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