Open AccessAutomated UHF RFID‐based book positioning and monitoring method in smart libraries
Author(s) -
Yaman Orhan,
Ertam Fatih,
Tuncer Turker,
Firat Kilincer Ilhan
Publication year - 2020
Publication title -
iet smart cities
Language(s) - English
Resource type - Journals
ISSN - 2631-7680
DOI - 10.1049/iet-smc.2020.0033
Subject(s) - subspace topology , ultra high frequency , computer science , support vector machine , k nearest neighbors algorithm , radio frequency identification , identification (biology) , artificial intelligence , random subspace method , ensemble learning , pattern recognition (psychology) , data mining , telecommunications , botany , computer security , biology
In this study, a method is proposed for ultra high frequency radio frequency identification (UHF RFID)‐based book positioning and counting developed for smart libraries. In the experimental setup created, RFID tags placed in books were automatically detected using three RFID antennas. Using received signal strength indicator information from each antenna for each book, the locations of the books are determined. In addition, classification was made by using machine learning approaches for the study. For this purpose, the best result for sequence determination in the classification study using ensemble trees, K nearest neighbours (KNN), and support vector machine algorithms was obtained with the ensemble subspace KNN algorithm with 94.1%. The best result for cabinet detection was obtained in the study using the ensemble subspace KNN algorithm and a 78.5% accuracy rate was achieved. The best result for rack detection was obtained with the ensemble subspace KNN algorithm with 95.4%. The study is thought to be useful in the automatic determination of the row, cabinet, and rack of books in smart libraries.