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Efficient Location Prediction in Mobile Cellular Networks

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Abstract

Mobile context-aware applications are capable of predicting the context of the user in order to operate pro-actively and provide advanced services. We propose an efficient spatial context classifier and a short-term predictor for the future location of a mobile user in cellular networks. We introduce different variants of the considered location predictor dealing with location (cell) identifiers and directions. Symbolic location classification is treated as a supervised learning problem. We evaluate the prediction efficiency and accuracy of the proposed predictors through synthetic and real-world traces and compare our solution with existing algorithms for location prediction. Our findings are very promising for the location prediction problem and the adoption of proactive context-aware applications and services.

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Authors and Affiliations

  1. Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15784, Athens, Greece

    Theodore Anagnostopoulos & Stathes Hadjiefthymiades

  2. Department of Informatics, Ionian University, 49100, Corfu, Greece

    Christos Anagnostopoulos

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  1. Theodore Anagnostopoulos
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  2. Christos Anagnostopoulos
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  3. Stathes Hadjiefthymiades
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Correspondence to Christos Anagnostopoulos.

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Anagnostopoulos, T., Anagnostopoulos, C. & Hadjiefthymiades, S. Efficient Location Prediction in Mobile Cellular Networks. Int J Wireless Inf Networks 19, 97–111 (2012). https://doi.org/10.1007/s10776-011-0166-9

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  • DOI: https://doi.org/10.1007/s10776-011-0166-9

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