Finding frequently visited paths: Dealing with the uncertainty of spatio-temporal mobility data

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    4 Citations (Scopus)
    89 Downloads (Pure)

    Abstract

    With the ever-increasing advancements in sensor technology and localization systems, large amounts of spatio-temporal data can be collected from moving objects equipped with wireless sensor nodes. Analysis of such data provides the opportunity of extracting useful information about movement behaviour and interaction between moving objects. Inherent characteristics of wireless sensor nodes cause the data collected by them to have low or irregular frequency and often be erroneous. Existence of different levels of uncertainty in these data makes the procedure of finding movement patterns difficult and ambiguous. In this paper, we propose a hierarchical approach to find the frequently visited paths using location data of people carrying a custom designed mobile wireless sensor node. We hierarchically cluster trajectories and find their resemblance at the finest level while dealing with the uncertainties. The performance evaluation results show that compared with previous schemes, our method performs better in presence of ambiguity and sources of data uncertainty.
    Original languageUndefined
    Title of host publicationIEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2013
    Place of PublicationUSA
    PublisherIEEE
    Pages479-484
    Number of pages6
    ISBN (Print)978-1-4673-5499-8
    DOIs
    Publication statusPublished - 2 Apr 2013
    Event8th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2013 - Melbourne, Australia
    Duration: 2 Apr 20135 Apr 2013
    Conference number: 8

    Publication series

    Name
    PublisherIEEE

    Conference

    Conference8th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2013
    Abbreviated titleISSNIP
    CountryAustralia
    CityMelbourne
    Period2/04/135/04/13

    Keywords

    • METIS-297728
    • IR-86812
    • DB-DM: DATA MINING
    • EWI-23509

    Cite this