RFID Integration with Internet of Things: Data Processing Algorithm Based on Convolutional Neural Network

Abstract

Radio Frequency Identification is a fast and reliable communication module that performs automatic data capture to identify and track individual objects and people. Frequency-coded tags employ resonant networks to decode their unique code. A multi-scatterer or multi-resonant method encodes the data. Research primarily related to the current investigation predicted that the chipless RFID tag resonant network has a high bit encoding capacity. This study addresses the simulation, optimization, fabrication, testing, and data encoding methods for chipless RFID tags. This research provides a framework for the open-ended quarter-wavelength stub multi-resonator method in chipless Radio Frequency Identification (RFID) tags. The proposed design enhances the tag's data encoding capacity and improves its robustness to ecological differences. This study integrates Error Correction Coding (ECC) and Adaptive Modulation Systems (AMS) employing Convolutional Neural Networks (CNN) to enhance the tag's performance. The AMS dynamically alters the modulation parameters based on channel states, while ECC improves data reliability. The results indicate efficient performance compared to traditional chipless RFID tags, highlighting the possibility of practical behavior in typical applications that necessitate reliable and high-capacity data transmission.