IoT-Driven Regression Tree Models for Efficient Microwave Dielectric Material Characterization: Addressing Non-Linear Cavity Sensing

Khusro A. Akhter Z. Jha A.K. Shamim A. Hashmi M.S.
2025 Institute of Electrical and Electronics Engineers Inc.

IEEE Internet of Things Journal
2025 #12 Issue 15 31891 - 31906 pp.

Interconnected microwave dielectric sensing nodes have the potential to revolutionize microwave material processing and design, where microwave dielectric materials characterization (MDMC) with high precision and rapid circuit design are crucial. This research presents an Internet of Things (IoT)-enabled automated MDMC system designed to tackle the nonlinearity challenges in the extended cavity perturbation regime. Utilizing a cylindrical cavity operating in TE111 mode at 5 GHz, the proposed MDMC system is extensively trained on a diverse range of materials through numerous full-wave 3-D electromagnetic simulations. The outputs, i.e., relative permittivity and loss tangent, are derived using advanced machine learning models, including decision tree (DT) and ensemble learning (EL). A comparative analysis that incorporates simulation, measurement, and predicted permittivity values across varying sample dimensions demonstrates the robustness and accuracy of the DT and EL model. This validates the effectiveness of our high-quality sensor node and sophisticated data processing techniques within an IoT-centric framework.

Complex permittivity , dielectric properties , machine learning (ML) , microwave material characterization , microwave material industry , microwave resonant sensing , regression tree model , smart industry

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Nazarbayev University, School of Engineering and Digital Sciences, Department of Electrical and Computer Engineering, Astana, 000010, Kazakhstan
King Abdullah University of Science and Technology, IMPACT LAB, CEMSE Division, Jeddah, 23955, Saudi Arabia
Directed Energy Research Center, Technology Innovation Institute, Abu Dhabi, United Arab Emirates
Indian Institute of Technology Tirupati, Department of Electrical Engineering, Tirupati, 517506, India

Nazarbayev University
King Abdullah University of Science and Technology
Directed Energy Research Center
Indian Institute of Technology Tirupati

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