Machine Learning as Tag Estimation Method for ALOHA-based RFID system

Rodić, Lea Dujić; Stansic, Ivo; Zovko, Kristina; Šolić, Petar

doi:10.23919/splitech52315.2021.9566455

Cited by 1 publication

(3 citation statements)

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“…To obtain valuable data for model comparison, Monte Carlo simulations were performed to produce an adequate number of possible scenarios that may happen during the interrogation procedure in DFSA. Detailed elaboration of the mathematical background of the Monte Carlo method that has been applied for this research has been elaborated in research prior to this one and presented in [11]. The selected approach for Monte Carlo simulations of the distribution of tags in the slots follows the research performed in studies [8] and [23].…”

Section: Methodsmentioning

confidence: 99%

“…That is why Random Forest has been used for numerous applications such as bioinformatics, chemoinformatics, 3D object recognition, traffic accident detection, intrusion detection systems, computer vision, image analysis, etc. [11,53].…”

Section: Random Forest Modelmentioning

confidence: 99%

“…In general, the MAC of RFID is random, and there are two widely used methods to achieve it: the first is a binary tree, and the second is the ALOHA-based protocol [4]. In the binary tree protocol, continuous YES/NO communication is achieved between a reader and tags, while with the ALOHA protocol tag initiates communication with a request from the reader [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Tag Estimation Method for ALOHA RFID System Based on Machine Learning Classifiers

et al. 2022

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In the last two decades, Radio Frequency Identification (RFID) technology has attained prominent performance improvement and has been recognized as one of the key enablers of the Internet of Things (IoT) concepts. In parallel, extensive employment of Machine Learning (ML) algorithms in diverse IoT areas has led to numerous advantages that increase successful utilization in different scenarios. The work presented in this paper provides a use-case feasibility analysis of the implementation of ML algorithms for the estimation of ALOHA-based frame size in the RIFD Gen2 system. Findings presented in this research indicate that the examined ML algorithms can be deployed on modern state-of-the-art resource-constrained microcontrollers enhancing system throughput. In addition, such utilization can cope with latency since the execution time is sufficient to meet protocol needs.

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Section: Methodsmentioning

confidence: 99%

Section: Random Forest Modelmentioning

confidence: 99%