Next Article in Journal
Resilient Modulus—Physical Parameters Relationship of Improved Red Clay by Dynamic Tri-Axial Test
Next Article in Special Issue
Improved Anti-Collision Algorithm for the Application on Intelligent Warehouse
Previous Article in Journal
Two-Dimensional Modeling of Pressure Swing Adsorption (PSA) Oxygen Generation with Radial-Flow Adsorber
Previous Article in Special Issue
Novel Designated Ownership Transfer with Grouping Proof
Open AccessArticle

False Positive RFID Detection Using Classification Models

u-SCM Research Center, Nano Information Technology Academy, Dongguk University, Seoul 04626, Korea
Department of Industrial and Systems Engineering, Dongguk University, Seoul 04620, Korea
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(6), 1154;
Received: 7 January 2019 / Revised: 13 March 2019 / Accepted: 13 March 2019 / Published: 19 March 2019
(This article belongs to the Special Issue Innovative RFID Applications)
Radio frequency identification (RFID) is an automated identification technology that can be utilized to monitor product movements within a supply chain in real-time. However, one problem that occurs during RFID data capturing is false positives (i.e., tags that are accidentally detected by the reader but not of interest to the business process). This paper investigates using machine learning algorithms to filter false positives. Raw RFID data were collected based on various tagged product movements, and statistical features were extracted from the received signal strength derived from the raw RFID data. Abnormal RFID data or outliers may arise in real cases. Therefore, we utilized outlier detection models to remove outlier data. The experiment results showed that machine learning-based models successfully classified RFID readings with high accuracy, and integrating outlier detection with machine learning models improved classification accuracy. We demonstrated the proposed classification model could be applied to real-time monitoring, ensuring false positives were filtered and hence not stored in the database. The proposed model is expected to improve warehouse management systems by monitoring delivered products to other supply chain partners. View Full-Text
Keywords: RFID; RSS; machine learning; classification; false positive; outlier detection RFID; RSS; machine learning; classification; false positive; outlier detection
Show Figures

Graphical abstract

MDPI and ACS Style

Alfian, G.; Syafrudin, M.; Yoon, B.; Rhee, J. False Positive RFID Detection Using Classification Models. Appl. Sci. 2019, 9, 1154.

AMA Style

Alfian G, Syafrudin M, Yoon B, Rhee J. False Positive RFID Detection Using Classification Models. Applied Sciences. 2019; 9(6):1154.

Chicago/Turabian Style

Alfian, Ganjar; Syafrudin, Muhammad; Yoon, Bohan; Rhee, Jongtae. 2019. "False Positive RFID Detection Using Classification Models" Appl. Sci. 9, no. 6: 1154.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop