Polymer Composite Nodes for Smart IoT Environmental Monitoring: Applying ML-Calibrated RF Sensors for Exceptionally Low-Power, Flexible, and Dependable Performance

Year : 2026 | Volume : 14 | Special Issue 01 | Page : 1205 1221
    By

    M. Selvaganapathy,

  • Kanimozhi Rajasekaran,

  • Helina Rajini Suresh,

  • A. Kanimozhi,

  • R. Balasubramaniyan,

  • K. Sathish,

  • K. Muthukannan,

  1. Associate Professor, Department of ECE, C. K. College of Engineering and Technology, Cuddalore, Tamil Nadu, India
  2. Associate Professor, Department of ECE, C. K. College of Engineering and Technology, Cuddalore, Tamil Nadu, India
  3. Associate Professor, Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India
  4. Associate Professor, Department of Information Technology Meenakshi Sundararajan Engineering College, Chennai, Tamil Nadu, India
  5. Assistant Professor, Department of Artificial Intelligence & Data Science Jeppiaar Institute of Technology, Chennai, Tamil Nadu, India
  6. Assistant Professor (SG), Department of Electronics and Communications Engineering Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, Tamil Nadu, India
  7. Associate Professor, Department of Computer Science and Engineering Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College, Chennai, Tamil Nadu, India

Abstract

The quick usage of the Internet of Things (IoT) for monitoring the environment needs sensor platforms that are accurate, responsive, adaptable, energy-efficient, and able to work in many different conditions. This work presents polymer composite nodes equipped with machine learning (ML)-calibrated radio-frequency (RF) sensors that adhere to stringent criteria. Sensor substrates built of flexible polymer matrices with conductive fillers are strong, light, and flexible enough to fit on surfaces that aren’t perfectly flat. These composite nodes keep their RF properties even when the temperature, humidity, and stress change. Over time, they will work the same in real life. The sensor pipeline incorporates a machine learning calibration framework that makes it more reliable and saves power. This technique changes the RF response in real time dependent on noise, the environment, and how materials react as they are bent. It heals itself and makes predictions without needing a lot of parts. The experimental results show that ML-calibrated RF nodes are sensitive and selective, and they use 40% less power than regular IoT sensor systems. The nodes are mechanically flexible because they are made of composites. This makes them perfect for wearable, deployed, or regional environmental monitoring applications. This research indicates that intelligent materials and adaptive AI calibration could yield reliable, energy-efficient, and flexible monitoring solutions. The suggested technique will let future IoT systems keep an eye on the weather, find contaminants, manage farms, and develop smart city infrastructure.

Keywords: Polymer composite nodes, RF sensors, machine learning calibration, environmental sensing, internet of things (IoT)

[This article belongs to Special Issue under section in Journal of Polymer & Composites (jopc)]

How to cite this article:
M. Selvaganapathy, Kanimozhi Rajasekaran, Helina Rajini Suresh, A. Kanimozhi, R. Balasubramaniyan, K. Sathish, K. Muthukannan. Polymer Composite Nodes for Smart IoT Environmental Monitoring: Applying ML-Calibrated RF Sensors for Exceptionally Low-Power, Flexible, and Dependable Performance. Journal of Polymer & Composites. 2026; 14(01):1205-1221.
How to cite this URL:
M. Selvaganapathy, Kanimozhi Rajasekaran, Helina Rajini Suresh, A. Kanimozhi, R. Balasubramaniyan, K. Sathish, K. Muthukannan. Polymer Composite Nodes for Smart IoT Environmental Monitoring: Applying ML-Calibrated RF Sensors for Exceptionally Low-Power, Flexible, and Dependable Performance. Journal of Polymer & Composites. 2026; 14(01):1205-1221. Available from: https://journals.stmjournals.com/jopc/article=2026/view=239472


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Special Issue Subscription Review Article
Volume 14
Special Issue 01
Received 15/10/2025
Accepted 06/11/2025
Published 13/02/2026
Publication Time 121 Days
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