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  • Intelligent High-Level RFID Event Transformation Utilising Non-Monotonic Reasoning

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    67412_1.pdf (183.9Kb)
    Author(s)
    Darcy, Peter
    Stantic, Bela
    Sattar, Abdul
    Griffith University Author(s)
    Stantic, Bela
    Sattar, Abdul
    Year published
    2010
    Metadata
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    Abstract
    Radio Frequency Identification technology utilises radio communication between tags and readers to automatically recognise large amounts of items reducing cost in both time and effort. Although there is a wide array of applications which RFID may be applied to, there are several issues that hinder the adoption of the technology into various commercial sectors. One of these problems is that the data recorded by readers are becoming increasingly difficult to decipher in its raw form resulting in complex spatio-temporal aspects. To counter the effects of this issue, we have proposed a concept that employs Clausal Defeasible ...
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    Radio Frequency Identification technology utilises radio communication between tags and readers to automatically recognise large amounts of items reducing cost in both time and effort. Although there is a wide array of applications which RFID may be applied to, there are several issues that hinder the adoption of the technology into various commercial sectors. One of these problems is that the data recorded by readers are becoming increasingly difficult to decipher in its raw form resulting in complex spatio-temporal aspects. To counter the effects of this issue, we have proposed a concept that employs Clausal Defeasible Logic Non-Monotonic Reasoning to correctly identify a high-level event. From our experimental evaluation, we have shown that our approach surpasses the state-of-the-art probabilistic method in both identifying events and limiting false-positive anomalies into the data set.
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    Conference Title
    2010 6TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS NETWORKING AND MOBILE COMPUTING (WICOM)
    DOI
    https://doi.org/10.1109/WICOM.2010.5601081
    Copyright Statement
    © 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    Subject
    Data engineering and data science
    Publication URI
    http://hdl.handle.net/10072/37285
    Collection
    • Conference outputs

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