Reinforcement Learning for Adaptive Sensing with Shape Memory Polymer-Based IoT Nodes

Year : 2025 | Volume : 13 | Special Issue 06 | Page : 370 391
    By

    Priyanka Vashisht,

  • Anvesha Katti,

  • Manna Sheela Rani Chetty,

  • Tarun Gehlot,

  • N. Sreekanth,

  • Harish Reddy Gantla,

  1. Associate Professor, Department of Computer Science and Engineering, Amity University, Haryana, India
  2. Assistant Professor, Department of Computer Science and Engineering, Amity University, Haryana, India
  3. Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, Andhra Pradesh, India
  4. Assistant Professor, Department of Civil Engineering, College of Technology and Agriculture Engineering, Agriculture University Jodhpur, Rajasthan, India
  5. Professor, Department of Electronics and Communication Engineering, G.Pullaiah College of Engineering and Technology, Kurnool, Andhra Pradesh, India
  6. Associate Professor, Department of Computer Science and Engineering, Vignan Institute of Technology and Science, Telangana, India

Abstract

The rapid expansion of intelligent sensing in the Internet of Things (IoT) has revealed the pressing need for materials and algorithms capable of self-adaptation in volatile environments. Conventional polymer-based sensors and static control strategies often fail to capture nonlinear thermo-mechanical dynamics, leaving them unsuitable for unpredictable operating conditions. Although prior studies have improved polymer composites or introduced algorithmic optimization independently, few attempts have coupled the adaptability of smart materials with machine-driven learning. This gap leaves IoT systems vulnerable to drift, energy inefficiency, and poor scalability when deployed at large scale. In this work, we introduce a reinforcement learning–driven framework that directly integrates shape memory polymer (SMP) composites into IoT sensing nodes. The novelty lies in embedding an RL policy engine within the material–device interface, enabling the system to continuously recalibrate sensing, actuation, and energy expenditure in response to environmental fluctuations. Unlike static calibration or rule-based heuristics, the proposed approach co-evolves with the dynamic response of SMPs, creating a dual-adaptive sensing architecture. Experimental evaluation demonstrates marked improvements: sensing accuracy exceeded 95%, latency reduced to 0.18 s, and network lifetime extended by nearly 22% relative to federated and static baselines. Moreover, emergent behaviors such as autonomous frequency modulation of sensing cycles revealed the system’s ability to anticipate variations rather than merely react. By fusing polymer adaptability with reinforcement intelligence, this study establishes a pathway toward intelligent matter—self-optimizing sensing platforms capable of functioning reliably in uncertain, resource-constrained domains spanning healthcare monitoring, aerospace structures, and smart infrastructure.

Keywords: Reinforcement learning, shape memory polymers, adaptive sensing, IoT nodes, smart materials, energy efficiency.

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

How to cite this article:
Priyanka Vashisht, Anvesha Katti, Manna Sheela Rani Chetty, Tarun Gehlot, N. Sreekanth, Harish Reddy Gantla. Reinforcement Learning for Adaptive Sensing with Shape Memory Polymer-Based IoT Nodes. Journal of Polymer and Composites. 2025; 13(06):370-391.
How to cite this URL:
Priyanka Vashisht, Anvesha Katti, Manna Sheela Rani Chetty, Tarun Gehlot, N. Sreekanth, Harish Reddy Gantla. Reinforcement Learning for Adaptive Sensing with Shape Memory Polymer-Based IoT Nodes. Journal of Polymer and Composites. 2025; 13(06):370-391. Available from: https://journals.stmjournals.com/jopc/article=2025/view=230670


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Special Issue Subscription Original Research
Volume 13
Special Issue 06
Received 23/08/2025
Accepted 02/09/2025
Published 12/09/2025
Publication Time 20 Days
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