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  • Resolving RFID Data Stream Collisions using Set-Based Approach

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    Author(s)
    Pupunwiwat, Prapassara
    Stantic, Bela
    Griffith University Author(s)
    Stantic, Bela
    Year published
    2010
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    Abstract
    Radio Frequency Identification (RFID) is a technology that incorporates the use of the electromagnetic spectrum to uniquely identify people or objects. RFID technology has major data stream issues caused by massive amounts of tags simultaneously being captured by the reader that results in collisions. This collision issue can be solved by using anti-collision methods. The current ALOHA-based methods suffer from insufficient performance, especially when a substantial number of tags are present within a reader zone. In this work, we propose a Modified Dynamic Framed-Slotted ALOHA (MDFSA) technique to maximise the performance ...
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    Radio Frequency Identification (RFID) is a technology that incorporates the use of the electromagnetic spectrum to uniquely identify people or objects. RFID technology has major data stream issues caused by massive amounts of tags simultaneously being captured by the reader that results in collisions. This collision issue can be solved by using anti-collision methods. The current ALOHA-based methods suffer from insufficient performance, especially when a substantial number of tags are present within a reader zone. In this work, we propose a Modified Dynamic Framed-Slotted ALOHA (MDFSA) technique to maximise the performance efficiency and to reduce the total number of slots queried during the tag identification process. The MDFSA approach creates new tag grouping rules using particular equations according to the optimal efficiency obtained for a specific number of tags. In this study, we have demonstrated that our proposed method has maintained its efficiency above other existing approaches, and has the most effective performance.
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    Conference Title
    Proceedings of the 2010 Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing
    DOI
    https://doi.org/10.1109/ISSNIP.2010.5706754
    Subject
    Data engineering and data science
    Publication URI
    http://hdl.handle.net/10072/37333
    Collection
    • Conference outputs

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