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  • Conceptual Selective RFID Anti-Collision Technique Management

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    Author(s)
    Pupunwiwat, Prapassara
    Darcy, Peter
    Stantic, Bela
    Griffith University Author(s)
    Stantic, Bela
    Year published
    2011
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    Abstract
    Radio Frequency Identification (RFID) uses wireless radio frequency technology to automatically identify tagged objects. Despite the extensive development of RFID technology, tag collisions still remains a major drawback. The collision issue can be solved by using anti-collision techniques. While existing research has focused on improving anti-collision methods alone, it is also essential that a suitable type of anti-collision algorithm is selected for the specific circumstance. In this work, we evaluate anti-collision techniques and perform a comparative analysis in order to find the advantages and disadvantages of each ...
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    Radio Frequency Identification (RFID) uses wireless radio frequency technology to automatically identify tagged objects. Despite the extensive development of RFID technology, tag collisions still remains a major drawback. The collision issue can be solved by using anti-collision techniques. While existing research has focused on improving anti-collision methods alone, it is also essential that a suitable type of anti-collision algorithm is selected for the specific circumstance. In this work, we evaluate anti-collision techniques and perform a comparative analysis in order to find the advantages and disadvantages of each approach. To identify the best anti-collision selection method in various scenarios, we have proposed two strategies for selective anti-collision technique management: a "Novel Decision Tree Strategy" and a "Six Thinking Hats Strategy". We have shown that the selection of the correct technique for specific scenarios improve the quality of the data collection which, in turn, will increase the integrity of the data after being transformed, aggregated, and used for event processing.
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    Journal Title
    Procedia Computer Science
    Volume
    5
    DOI
    https://doi.org/10.1016/j.procs.2011.07.114
    Copyright Statement
    © 2011 Elsevier. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
    Subject
    Information and computing sciences
    Data engineering and data science
    Publication URI
    http://hdl.handle.net/10072/43606
    Collection
    • Journal articles

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