Location prediction based on a sector snapshot for location-based services

DAOUD, Mohammad Sharif, AYESH, Aladdin, AL-FAYOUMI, Mustafa and HOPGOOD, Adrian (2014). Location prediction based on a sector snapshot for location-based services. Journal of Network and Systems Management, 22 (1), 23-49.

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Link to published version:: https://doi.org/10.1007/s10922-012-9258-9
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    Abstract

    In location-based services (LBSs), the service is provided based on the users' locations through location determination and mobility realization. Most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shaped cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. Such techniques are the new Markov-based mobility prediction (NMMP) and prediction location model (PLM) that deal with inner cell structure and different levels of prediction, respectively. The NMMP and PLM techniques suffer from complex computation, accuracy rate regression, and insufficient accuracy. In this paper, a novel cell splitting algorithm is proposed. Also, a new prediction technique is introduced. The cell splitting is universal so it can be applied to all types of cells. Meanwhile, this algorithm is implemented to the Micro cell in parallel with the new prediction technique. The prediction technique, compared with two classic prediction techniques and the experimental results, show the effectiveness and robustness of the new splitting algorithm and prediction technique.

    Item Type: Article
    Research Institute, Centre or Group - Does NOT include content added after October 2018: Materials and Engineering Research Institute > Centre for Automation and Robotics Research > Mobile Machine and Vision Laboratory
    Identification Number: https://doi.org/10.1007/s10922-012-9258-9
    Page Range: 23-49
    Depositing User: Adrian Hopgood
    Date Deposited: 02 Aug 2013 13:47
    Last Modified: 11 May 2018 19:13
    URI: http://shura.shu.ac.uk/id/eprint/7190

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