Autonomic Computing and Communications Systems. Third International ICST Conference, Autonomics 2009, Limassol, Cyprus, September 9-11, 2009, Revised Selected Papers

Research Article

An Online Adaptive Model for Location Prediction

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  • @INPROCEEDINGS{10.1007/978-3-642-11482-3_5,
        author={Theodoros Anagnostopoulos and Christos Anagnostopoulos and Stathes Hadjiefthymiades},
        title={An Online Adaptive Model for Location Prediction},
        proceedings={Autonomic Computing and Communications Systems. Third International ICST Conference, Autonomics 2009, Limassol, Cyprus, September 9-11, 2009, Revised Selected Papers},
        proceedings_a={AUTONOMICS},
        year={2012},
        month={4},
        keywords={Context-awareness location prediction Machine Learning online clustering classification Adaptive Resonance Theory},
        doi={10.1007/978-3-642-11482-3_5}
    }
    
  • Theodoros Anagnostopoulos
    Christos Anagnostopoulos
    Stathes Hadjiefthymiades
    Year: 2012
    An Online Adaptive Model for Location Prediction
    AUTONOMICS
    Springer
    DOI: 10.1007/978-3-642-11482-3_5
Theodoros Anagnostopoulos1,*, Christos Anagnostopoulos1,*, Stathes Hadjiefthymiades1,*
  • 1: University of Athens
*Contact email: thanag@di.uoa.gr, bleu@di.uoa.gr, shadj@di.uoa.gr

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 Means and Online Means algorithms. Our findings are very promising for the use of the proposed model in mobile context aware applications.