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

Title: 在分子通信环境中的智能沟通：生物纳米物联网中的神经网络

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. Show Chinese abstract

Abstract: 近年来，生物纳米物体互联网（IoBNT）的最新发展正在为医疗保健领域的创新应用奠定基础。 设计用于在体内运行并通过互联网远程管理的纳米设备，旨在迅速检测并处理潜在疾病。 在这种设想中，由于单个纳米传感器的能力有限，会出现一个固有的挑战；具体来说，纳米传感器必须相互通信以作为集群进行协作。 旨在研究集群能力的边界，本综述强调了在分子通信（MC）信道中使用数据驱动的通信策略，作为连接纳米传感器的一种手段。 依赖于机器学习（ML）方法的灵活性和鲁棒性来应对MC信道的动态特性，MC研究社区经常参考神经网络（NN）架构。 这一跨学科研究领域涵盖了多个方面，包括使用NNs促进MC环境中的通信、它们在纳米尺度上的实现、NNs的可解释方法以及用于训练的数据集生成。 在本综述中，我们对MC中NN架构的最新趋势的基本观点进行了全面分析，其在纳米尺度上实现的可行性，应用的可解释人工智能（XAI）技术，以及数据集的可访问性及生成的最佳实践。 此外，我们提供了开源代码仓库，以说明基于NN的方法，从而支持关键MC场景的可重复研究。 最后，我们识别了新兴的研究挑战，如稳健的NN架构、与生物集成的NN模块以及可扩展的训练策略。