Communicating Smartly in Molecular Communication Environments: Neural Networks in the Internet of Bio-Nano Things
Journal:
arXiv
Published Date:
Jun 25, 2025
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.
