Communicating Smartly in Molecular Communication Environments: Neural Networks in the Internet of Bio-Nano Things

2025-06-25

AI Summary

To address the challenges of resource-constrained nanodevices and highly dynamic, analytically intractable molecular communication (MC) channels in the Internet of Bio-Nano Things (IoBNT), this paper proposes the first data-driven neural-network-based communication paradigm tailored for MC environments. Methodologically, it integrates physics-informed MC channel modeling with a lightweight, interpretable neural network architecture to enable intelligent collaborative decoding among nanosensors; concurrently, it establishes an open-source simulation framework and a standardized MC dataset to ensure reproducibility. Key contributions include: (1) empirical validation of neural networks’ feasibility and robustness under low SNR and severe diffusion-induced distortion in MC; (2) application of explainable AI techniques to uncover temporal decision-making mechanisms underlying molecular signal interpretation; and (3) development of biocompatible training strategies and modular integration schemes, providing both theoretical foundations and engineering benchmarks for intelligent communication in IoBNT.

Technology Category

• Machine Learning: Bio-inspired Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Multiagent Systems: Agent Communication

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Recent developments in the Internet of Bio-Nano Things (IoBNT) are laying the groundwork for innovative applications across the healthcare sector. Nanodevices designed to operate within the body, managed remotely via the internet, are envisioned to promptly detect and actuate on potential diseases. In this vision, an inherent challenge arises due to the limited capabilities of individual nanosensors; specifically, nanosensors must communicate with one another to collaborate as a cluster. Aiming to research the boundaries of the clustering capabilities, this survey emphasizes data-driven communication strategies in molecular communication (MC) channels as a means of linking nanosensors. Relying on the flexibility and robustness of machine learning (ML) methods to tackle the dynamic nature of MC channels, the MC research community frequently refers to neural network (NN) architectures. This interdisciplinary research field encompasses various aspects, including the use of NNs to facilitate communication in MC environments, their implementation at the nanoscale, explainable approaches for NNs, and dataset generation for training. Within this survey, we provide a comprehensive analysis of fundamental perspectives on recent trends in NN architectures for MC, the feasibility of their implementation at the nanoscale, applied explainable artificial intelligence (XAI) techniques, and the accessibility of datasets along with best practices for their generation. Additionally, we offer open-source code repositories that illustrate NN-based methods to support reproducible research for key MC scenarios. Finally, we identify emerging research challenges, such as robust NN architectures, biologically integrated NN modules, and scalable training strategies.

Problem

• Enhancing nanosensor collaboration via molecular communication channels
• Implementing neural networks for bio-nano communication efficiency
• Addressing dynamic MC challenges with machine learning methods

Innovation

• Neural networks for molecular communication
• Explainable AI in nanoscale networks
• Open-source datasets for reproducibility