Introspection-based Periodicity Awareness Model for Intermittently Connected Mobile Networks

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    Abstract

    Recently, context awareness in Intermittently Connected Mobile Networks (ICMNs) has gained popularity in order to discover social similarities among mobile entities. Nevertheless, most of the contextual methods depend on network knowledge obtained with unrealistic scenarios. Mobile entities should have a self-knowledge determination in order to estimate their activity routines in a group of communities. This paper presents a periodicity awareness model which relies on introspective spatiotemporal observations. In this model, hourly, daily, and weekly locations of mobile entities are being tracked to predict future trajectories and periodicities within a targeted time period. Realistic simulations are utilized to analyze the predictions in weekly observation sets. The results show that a reasonable accuracy with an increasing level of determination can be obtained which does not require global network knowledge. In this regard, the presented model can give insights for any type of ICMN objectives.
    Original languageUndefined
    Title of host publicationProceedings of the Fourth International Conference on Mobile, Ubiquitous, and Intelligent Computing, MUSIC 2013
    Place of PublicationBerlin
    PublisherSpringer
    Pages397-403
    Number of pages6
    ISBN (Print)978-3-642-40675-1
    DOIs
    Publication statusPublished - Sep 2013

    Publication series

    NameLecture Notes in Electrical Engineering
    PublisherSpringer Verlag
    Number274
    Volume274
    ISSN (Print)1876-1100

    Keywords

    • spatiotemporal correlations
    • EWI-23560
    • Intermittently-connected mobile networks
    • IR-87096
    • Social Networks
    • Context awareness
    • METIS-297764
    • periodicity awareness model

    Cite this

    Türkes, O., Scholten, J., & Havinga, P. J. M. (2013). Introspection-based Periodicity Awareness Model for Intermittently Connected Mobile Networks. In Proceedings of the Fourth International Conference on Mobile, Ubiquitous, and Intelligent Computing, MUSIC 2013 (pp. 397-403). (Lecture Notes in Electrical Engineering; Vol. 274, No. 274). Berlin: Springer. https://doi.org/10.1007/978-3-642-40675-1_61