Automated book location and classification method using RFID tags for smart libraries

Abstract

Purpose: There are hundreds of thousands of books in libraries, causing problems in searching and counting books. To overcome these problems, this research presents a received signal strength indicator (RSSI) dataset and a new automated book location classification model using the RSSI dataset. Materials and method: In this study, a RSSI signals dataset was collected using a mobile vehicle from 3279 books. These books are randomly placed on six benches, 24 cabinets, and 144 racks. The primary objective of this work is to detect book position automatically using a simple learning model. Thus, a new automated book position detection model is presented and this model is tested on the RSSI dataset collected. (i) Multiscale principal component analysis (MSPCA) is considered a pre-processing method. (ii) Iterative minimum redundancy maximum relevance (ImRMR) algorithm was used to select the most informative features automatically. (iii) The most informative features selected are classified using the Decision Trees (DT) and Ensemble Bagged Trees (BT) algorithms. Three cases are defined according to bench, cabinet, and rack. Results: The recommended model yielded 97.34% 98.26% and 93.1% overall accuracies using the bench, cabinet, and rack cases consecutively deploying the BT classifier with 10-fold cross-validation. Conclusions: The presented book position classification model reached above 90% for all cases and these results calculated and finding clearly demonstrates that this model is ready to use in a big library for book position detection.