Automated UHF RFID‐based book positioning and monitoring method in smart libraries. Issue 4 (15th October 2020)

• Record Type: Journal Article
• Authors: Yaman, Orhan; Ertam, Fatih; Tuncer, Turker; Firat Kilincer, Ilhan
• Is Part Of: IET smart cities. Volume 2:Issue 4(2020)
• Page Start: 173
• Page End: 180
• Publication Date: 2020-10-15
• Subjects: radiofrequency identification -- learning (artificial intelligence) -- support vector machines -- library automation -- trees (mathematics) -- nearest neighbour methods -- pattern classification -- RSSI -- UHF antennas; smart libraries -- RFID tags -- RFID antennas -- received signal strength indicator information -- classification study -- support vector machine algorithms -- ensemble subspace KNN algorithm -- UHF RFID‐based book positioning -- UHF RFID‐based book monitoring -- sequence determination -- ensemble trees -- K nearest neighbours -- rack detection; Smart cities -- Periodicals; City planning -- Technological innovations -- Periodicals; Cities and towns -- Growth -- Periodicals; Sustainable urban development -- Periodicals; Sustainable development; City planning -- Technological innovations; Cities and towns -- Growth; Periodicals; 307.76
• DOI: 10.1049/iet-smc.2020.0033
• Languages: English
• ISSNs: 2631-7680

Abstract

Abstract : 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.