volume | PIER Journals

Abstract

Chipless RFID technology offers a cost-effective and durable alternative to chipped tags for identification and tracking applications. By eliminating the need for an integrated circuit, chipless tags are cheaper and can withstand harsher environments. This opens doors to not only track items throughout a supply chain or monitor valuable assets, but also integrate basic sensors for functionalities like environmental monitoring or smart agriculture. However, limitations in data capacity, read range, and decoding complexity currently hinder their full potential. This paper explores the application of machine learning techniques to improve the interrogation process and enhance the reliability of chipless Radio Frequency Identification systems. The effectiveness of machine learning in optimising chipless RFID systems hinges on the richness and variety of training data. A robust dataset encompassing diverse tag characteristics, environmental factors, and reader configurations is paramount. Nevertheless, gathering real-world RFID data can be difficult. To address this, a data collection procedure has been specifically designed to gather backscattered information from the chipless tags at multiple orientations and distances. Four binary combinations of a 5-bit RFID tag based on frequency-selective surfaces operating in the 2–8GHz range are considered for generating the database. The dataset is then used to train and validate various classification models, including support vector machine (SVM), k-nearest neighbour (k-NN), Decision Tree (DT), Naive Bayes classifier, and Logistic Regression (LR). The proposed Support Vector Machine model is able to identify the tag at a distance of up to 70 cm from the interrogator, with multiple rotational degrees of freedom.

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Mr. Athul Thomas

Dr. Midhun Muraleedharan Sylaja

Dr. James Kurian