Predicting the location of mobile users: a machine learning approach

Authors:
Theodoros Anagnostopoulos;Christos Anagnostopoulos;Stathes Hadjiefthymiades;Miltos Kyriakakos;Alexandros Kalousis
Affiliations:
Department of Informatics and Telecommunications, University of Athens, Athens, Greece;Department of Informatics and Telecommunications, University of Athens, Athens, Greece;Department of Informatics and Telecommunications, University of Athens, Athens, Greece;Department of Informatics and Telecommunications, University of Athens, Athens, Greece;Department of Computer Science, University of Geneva, Geneva, Switzerland
Venue:
Proceedings of the 2009 international conference on Pervasive services
Year:
2009

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Abstract

Mobile context-aware applications experience a constantly changing environment with increased dynamicity. In order to work efficiently, the location of mobile users needs to be predicted and properly exploited by mobile applications. We propose a spatial context model, which deals with the location prediction of mobile users. Such model is used for the classification of the users' trajectories through Machine Learning (ML) algorithms. Predicting spatial context is treated through supervised learning. We evaluate our model in terms of prediction accuracy w.r.t. specific prediction parameters. The proposed model is also compared with other ML algorithms for location prediction. Our findings are very promising for the efficient operation of mobile context-aware applications.