Enhancing counterfeit tag detection for RFID system using slotted aloha

Musa, Yusuf (2021) Enhancing counterfeit tag detection for RFID system using slotted aloha. Masters thesis, Universiti Tun Hussein Malaysia.

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Radio Frequency Identification (RFID) is a technology that is used for wireless monitoring, tracking and identification of an object. RFID is also used in inventory processes for RFID counterfeit detection and identification to reduce financial loss caused by shrinkage. Counterfeiting is a serious challenge in the current supply chain, resulting in millions of dollars in lost income for the parties involved each year. The recent works on counterfeit detection are based on cardinality contradiction and cloned detection which assumed that both counterfeit and genuine tags most be in the system before it can detect counterfeit. However, this assumption will not work if one of the tags is not in the system. As a result of this research, RMD was proposed, which entails the usage of the SLOTTED ALOHA in order to improve the detection time and accuracy through collision verification and counterfeit detection. The collision verification was introduced at the first stage to increase the status of single slots which gives the system the ability to uniquely detect each tag without arbitration. The track sequence number (TSN) was integrated with the time of arrival (TOA) at the detection stage to check the sequence process for the entire system from one stage to another, thus giving the system the capability to uniquely differentiate between counterfeit tag and the genuine tag. In comparison with FSA- CSS, MAP, and DFSA, experimental results show that the RMD algorithm outperforms other algorithms with high throughput performance of 45 unit/s, an average detection accuracy of 98% and low detection time of 0.10s. This indicates that highest detection accuracy, high throughput performance and lowest detection time (i.e., fast detection) can effectively be achieved by RMD algorithm. The optimization of the counterfeit tags detection algorithm with TSN and TOA is therefore competent and sufficient for addressing the shrinkage of inventory.

Item Type: Thesis (Masters)
Subjects: T Technology > T Technology (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Mrs. Sabarina Che Mat
Date Deposited: 06 Feb 2022 21:22
Last Modified: 06 Feb 2022 21:22
URI: http://eprints.uthm.edu.my/id/eprint/6497

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