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<p>In recent years, advances in nanotechnology and the Internet of Things (IoT) have led to the development of the revolutionary Internet of Nano Things (IoNT). IoNT, has found very similar real-life applications in agriculture, military, multimedia, and healthcare. However, despite the rapid advancements in both IoNT and machine learning (ML), there has been no comprehensive review explicitly focused on the integration of these two fields. Existing surveys and reviews on IoNT primarily address its architecture, communication methods, and domain-specific applications, yet overlook the critical role ML could play in enhancing IoNT’s capabilities–particularly in data processing, anomaly detection, and security. This survey addresses this gap by providing an in-depth analysis of IoNT-ML integration, reviewing state-of-the-art ML applications within IoNT, and systematically discussing the challenges that persist in this integration. Additionally, we propose future research directions, establishing a framework to guide advancements in IoNT through ML-driven solutions.</p>

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A comprehensive review of machine learning applications for internet of nano things: challenges and future directions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月11日

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  • Aryan Rana,
  • Deepika Gautam,
  • Pankaj Kumar,
  • Kranti Kumar,
  • Athanasios V. Vasilakos,
  • Ashok Kumar Das,
  • Vivekananda Bhat K

摘要

In recent years, advances in nanotechnology and the Internet of Things (IoT) have led to the development of the revolutionary Internet of Nano Things (IoNT). IoNT, has found very similar real-life applications in agriculture, military, multimedia, and healthcare. However, despite the rapid advancements in both IoNT and machine learning (ML), there has been no comprehensive review explicitly focused on the integration of these two fields. Existing surveys and reviews on IoNT primarily address its architecture, communication methods, and domain-specific applications, yet overlook the critical role ML could play in enhancing IoNT’s capabilities–particularly in data processing, anomaly detection, and security. This survey addresses this gap by providing an in-depth analysis of IoNT-ML integration, reviewing state-of-the-art ML applications within IoNT, and systematically discussing the challenges that persist in this integration. Additionally, we propose future research directions, establishing a framework to guide advancements in IoNT through ML-driven solutions.