IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 14, No 4: August 2025

Lightweight mutual authentication protocol for resource-constrained radio frequency identification tags with PRINCE cipher

Naik, Mahendra Shridhar (Unknown)
Sreekantha, Desai Karanam (Unknown)
Sairam, Kanduri V S S S S (Unknown)
Nataraju, Chaitra Soppinahally (Unknown)



Article Info

Publish Date
01 Aug 2025

Abstract

Radio frequency identification (RFID) is a key technology for the internet of things (IoT), with widespread applications in the commercial, healthcare, enterprise, and community sectors. However, privacy and security concerns remain with RFID systems. This manuscript presents a novel RFID-based mutual authentication protocol (MAP) using the PRINCE cipher to address these concerns. The proposed MAP leverages a PRINCE cipher architecture capable of both encryption and decryption based on a mode signal. It performs five encryption and two decryption processes during tag and reader mutual authentication, with updated seed values ensuring synchronization and secure data communication. The PRINCE cipher implementation utilizes less than 1% of slices, operates at 226 MHz with a latency of 3.5 clock cycles (CC), and has a throughput of 4.125 Gbps. The complete RFID-based MAP consumes 721 mW of power, occupies 2% of the chip area, and achieves a latency of 35.5 CC and a throughput of 262 Mbps. This represents a 25% reduction in latency, a 40% increase in throughput, and a 30% decrease in execution time compared to existing MAP approaches. The findings demonstrate the potential of the proposed MAP to enhance latency, throughput, and execution time, offering a promising solution for secure and efficient RFID authentication.

Copyrights © 2025






Journal Info

Abbrev

IJAI

Publisher

Subject

Computer Science & IT Engineering

Description

IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like ...