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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)

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.

Keywords

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

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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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