Advances in Computer Science and Information Technology. Networks and Communications. Second International Conference, CCSIT 2012, Bangalore, India, January 2-4, 2012. Proceedings, Part I

Research Article

A Location Dependent Semantic Cache Replacement Strategy in Mobile Environment

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  • @INPROCEEDINGS{10.1007/978-3-642-27299-8_23,
        author={Kahkashan Tabassum and Mahmood Syed and A. Damodaram},
        title={A Location Dependent Semantic Cache Replacement Strategy in Mobile Environment},
        proceedings={Advances in Computer Science and Information Technology. Networks and Communications. Second International Conference, CCSIT 2012, Bangalore, India, January 2-4, 2012. Proceedings, Part I},
        proceedings_a={CCSIT PART I},
        year={2012},
        month={11},
        keywords={Mobile Computing LDIS LDQ LDD Semantic Caching FAR ERBFNN},
        doi={10.1007/978-3-642-27299-8_23}
    }
    
  • Kahkashan Tabassum
    Mahmood Syed
    A. Damodaram
    Year: 2012
    A Location Dependent Semantic Cache Replacement Strategy in Mobile Environment
    CCSIT PART I
    Springer
    DOI: 10.1007/978-3-642-27299-8_23
Kahkashan Tabassum,*, Mahmood Syed,*, A. Damodaram,*
    *Contact email: kahkashan@mjcollege.ac.in, syedmahmood.q@gmail.com, adamodaram@jntuh.ac.in

    Abstract

    Mobile computing is developing fast and one of its major services is location dependent information services (LDIS).The dependence of the results of a query on the present location of the mobile user leads to such services. The query is called Location Dependent Query and the resultant data is called Location Dependent Data (LDD).The caching scheme often used in these services is semantic caching where information about data is stored along with data in cache. In this paper, we have added a new dimension, segment frequency (S) to the Semantic segment. The cache replacement policy takes this dimension into consideration when replacing the cache. The prediction algorithm Enhanced RBFNN (ERBFNN) takes the future location of neighbors into account. The existing FAR algorithm is modified taking into account the new dimension to replace items from cache. The proposed system is called Enhanced RBF-FAR (ERBF-FAR) algorithm. The experimental results show that the proposed system performs better and yields better results.