A Comprehensive Review of Machine Learning Applications for Internet of Nano Things: Challenges and Future Directions

Engineering Nanoscience Electrical Engineering Artificial Intelligence Chemistry Information Science Computer Science
Machine Learning Internet of Nano Things Data Security Anomaly Detection Nanoscale Communication

Academic Background

In recent years, the rapid development of nanotechnology and the Internet of Things (IoT) has given rise to a revolutionary field—the Internet of Nano Things (IoNT). The IoNT connects nanoscale devices to the internet, enabling them to play significant roles in areas such as agriculture, military, multimedia, and healthcare. However, despite significant advancements in both IoNT and machine learning (ML), comprehensive research on their integration remains relatively scarce. Existing studies primarily focus on the architecture, communication methods, and domain-specific applications of IoNT, often overlooking the potential of ML in data processing, anomaly detection, and security. Therefore, this paper aims to fill this gap by providing an in-depth analysis of the integration of IoNT and ML, exploring the latest applications of ML in IoNT, and systematically discussing the challenges associated with this integration.

Source of the Paper

This paper is co-authored by Aryan Rana, Deepika Gautam, Pankaj Kumar, Kranti Kumar, Athanasios V. Vasilakos, Ashok Kumar Das, and Vivekananda Bhat K. The authors are affiliated with various research institutions, including several universities in India and internationally renowned research organizations. The paper was accepted on March 24, 2025, and published in the journal Artificial Intelligence Review with the DOI 10.1007/s10462-025-11211-z.

Main Content of the Paper

1. Architecture of IoNT

The architecture of IoNT consists of multiple layers, including nanodevices, system architecture, and layered network architecture. Nanodevices are the fundamental units of IoNT, typically composed of control units, communication units, power units, data processing and storage units, reproduction units, as well as sensors, actuators, and transceivers. These devices are connected to gateway devices through nano routers (NRs) and nano-micro interface devices (NMID), ultimately enabling communication with the internet. The architectural design of IoNT aims to optimize communication efficiency between nanodevices, ensuring reliable data transmission and processing.

2. Nano Communication Technologies

The communication technologies of IoNT primarily include electromagnetic nano communication (EMNC) and molecular communication (MC). EMNC utilizes electromagnetic waves in the terahertz (THz) frequency band for data transmission, making it suitable for short-distance, high-bandwidth communication scenarios. MC, on the other hand, transmits information through chemical signals, particularly suitable for communication among nanodevices within biological systems. Additionally, emerging communication technologies such as acoustic nano communication, human body communication (HBC), and Förster resonance energy transfer (FRET) have also found applications in IoNT.

3. Applications of Machine Learning in IoNT

The applications of ML in IoNT are mainly reflected in data processing, anomaly detection, and security enhancement. Specifically, ML algorithms can optimize the data processing capabilities of nanodevices, improving the efficiency and accuracy of data transmission. Furthermore, ML can detect anomalous behaviors by analyzing large volumes of data, thereby enhancing the security of IoNT. This paper provides a detailed discussion of various ML algorithms applied in IoNT, including shallow learning (SL), deep learning (DL), and reinforcement learning (RL).

4. Challenges and Future Research Directions

Despite the promising prospects of ML applications in IoNT, several challenges remain. First, the limited computational and storage capabilities of nanodevices make it difficult to support complex ML algorithms. Second, the reliability and stability of nano communication technologies need further improvement. Lastly, the security and privacy issues of IoNT also require urgent attention. To address these challenges, this paper proposes several future research directions, including the development of more efficient ML algorithms, optimization of nano communication technologies, and enhancement of security measures for IoNT.

Significance and Value of the Paper

The publication of this paper fills the gap in research on the integration of IoNT and ML, providing valuable references for researchers in related fields. Through a comprehensive analysis of IoNT architecture, communication technologies, and ML applications, this paper not only offers theoretical support for the development of IoNT but also provides solutions to technical challenges in practical applications. Moreover, the future research directions proposed in this paper provide clear guidance for subsequent studies, facilitating further integration of IoNT and ML.

Highlights of the Paper

  1. Comprehensiveness: This paper is the first to provide a comprehensive review of the integration of IoNT and ML, covering multiple aspects such as architecture, communication technologies, and applications.
  2. Forward-Looking: In addition to summarizing existing research, this paper proposes future research directions, offering important references for subsequent studies.
  3. Practicality: This paper discusses the applications of ML in IoNT in detail, providing solutions to technical challenges in practical applications.

Other Valuable Information

The paper also includes a detailed list of abbreviations to help readers better understand the specialized terminology used. Furthermore, the charts and tables in the paper provide intuitive data support, enhancing its readability and persuasiveness.

This paper offers significant theoretical support and practical guidance for research on the integration of IoNT and ML, demonstrating high academic and practical value.