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Tag Estimation Method for ALOHA RFID System Based on Machine Learning Classifiers
Content Provider | MDPI |
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Author | Lea, Dujić Rodić Ivo, Stančić Toni, Perković Petar, Šolić Zovko, Kristina |
Copyright Year | 2022 |
Description | 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. |
Starting Page | 2605 |
e-ISSN | 20799292 |
DOI | 10.3390/electronics11162605 |
Journal | Electronics |
Issue Number | 16 |
Volume Number | 11 |
Language | English |
Publisher | MDPI |
Publisher Date | 2022-08-19 |
Access Restriction | Open |
Subject Keyword | Electronics Industrial Engineering Information and Library Science Internet of Things Rfid Tags Rfid Reader Machine Learning Tag Estimate Method Microcontroller |
Content Type | Text |
Resource Type | Article |