Tag Estimation Method for ALOHA RFID System Based on Machine Learning Classifiers
Abstract
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.
Type
Publication
Electronics, 11(16), 2605
Status
Peer-reviewed
Open access
Authors
Full Professor
I am a Full Professor in the
Department of Electronics and Computing
at the Faculty of Electrical Engineering, Mechanical Engineering and Naval
Architecture (FESB), University of Split,
Croatia, where I teach
wireless network security, wireless communication networks, data science and
reinforcement learning.
My research spans information security, applied cryptography and the Internet of
Things. Before returning to FESB in 2020, I was an assistant and then associate
professor at the University Department of Forensic Sciences
in Split. I co-founded the SpliTech international
conference and serve on the editorial board of the
Journal of Communications Software and Systems.