An Online Adaptive Model for Location Prediction

  • Theodoros Anagnostopoulos
  • Christos Anagnostopoulos
  • Stathes Hadjiefthymiades
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 23)


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.


Context-awareness location prediction Machine Learning online clustering classification Adaptive Resonance Theory 


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  1. 1.
    Dey, A.: Understanding and using context. Personal and Ubiquitous Computing 5(1), 4–7 (2001)CrossRefGoogle Scholar
  2. 2.
    Hightower, J., Borriello, G.: Location Systems for Ubiquitous Computing. IEEE Computer 34(8) (August 2001)Google Scholar
  3. 3.
    Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2004)zbMATHGoogle Scholar
  4. 4.
    Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley-Interscience, Hoboken (2001)zbMATHGoogle Scholar
  5. 5.
    Belogay, E., Cabrelli, C., Molter, U., Shonkwiler, R.: Calculating the Hausdorff Distance between Curves. Information Processing Letters 64(1), 17–22 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
  7. 7.
    Choi, S., Shin, K.G.: Predictive and adaptive bandwidth reservation for hand-offs in QoS-sensitive cellular networks. In: ACM SIGCOMM (1998)Google Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    Karmouch, A., Samaan, N.: A Mobility Prediction Architecture Based on Contextual Knowledge and Spatial Conceptual Maps. IEEE Trans. on Mobile Computing 4(6) (2005)Google Scholar
  10. 10.
    Viayan, R., Holtman, J.: A model for analyzing handoff algorithms. IEEE Trans. on Veh. Technol. 42(3) (August 1993)Google Scholar
  11. 11.
    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)Google Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    Curewitz, K.M., Krishnan, P., Vitter, J.S.: Practical Prefetching via Data Compression. In: Proceedings of ACM SIGMOD, pp. 257–266 (1993)Google Scholar
  14. 14.
    Narendra, K., Thathachar, M.A.L.: Learning Automata – An Introduction. Prentice Hall, Englewood Cliffs (1989)zbMATHGoogle Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    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)Google Scholar
  17. 17.
    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)CrossRefGoogle Scholar
  18. 18.
    Tao, Y., Faloutsos, C., Papadias, D., Liu, B.: Prediction and Indexing of Moving Objects with Unknown Motion Patterns. In: ACM SIGMOD (2004)Google Scholar
  19. 19.
    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)Google Scholar
  20. 20.
    Xiao, Y., Zhang, H., Wang, H.: Location Prediction for Tracking Moving Objects Based on Grey Theory. In: IEEE FSKD 2007 (2007)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2010

Authors and Affiliations

  • Theodoros Anagnostopoulos
    • 1
  • Christos Anagnostopoulos
    • 1
  • Stathes Hadjiefthymiades
    • 1
  1. 1.Pervasive Computing Research Group, Communication Networks Laboratory, Department of Informatics and TelecommunicationsUniversity of AthensAthensGreece

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