Advances in artificial intelligence (AI) and wireless technology are driving forward the large deployment of interconnected smart technologies that constitute cyber–physical systems (CPSs) and Internet of Things (IoT) for many commercial and military applications. CPS is characterized by communication, computing, and control engineering based on a large volume of data originating from various devices, plants, sensors, etc. Wireless technologies have enabled the ease of networking and communications for both CPS and IoT, by providing massive and critical connectivity and control mechanisms. However, they are prone to challenges, such as low latency, throughput, and scheduling. Recent research trends focus on how to intelligently use data from CPS units to enhance wireless connectivity in CPS. AI tools, particularly AI systems and machine learning (ML) algorithms, have been widely applied in the literature to develop efficient schemes for wireless CPS/IoT. This article presents a review on the role of AI in wireless networking for CPS and IoT. In particular, we focus on ML paradigms, such as transfer learning (TL), distributed learning, and federated learning, that have evolved as building blocks for the utilization of large data for learning, adaptation, and predictions in CPS and IoT systems that leverage wireless networking. Furthermore, we also highlight challenges faced by current and future wireless networks pertaining to CPS/IoT, which are yet to be addressed.

中文翻译：

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无线物联网和网络物理系统人工智能的最新进展：综合调查


人工智能（AI）和无线技术的进步正在推动互联智能技术的大规模部署，这些技术构成了许多商业和军事应用的网络物理系统（CPS）和物联网（IoT）。 CPS 的特点是基于来自各种设备、工厂、传感器等的大量数据进行通信、计算和控制工程。无线技术通过提供大量关键数据，使 CPS 和物联网的联网和通信变得更加容易。连接和控制机制。然而，它们很容易面临挑战，例如低延迟、吞吐量和调度。最近的研究趋势集中在如何智能地使用来自 CPS 单元的数据来增强 CPS 中的无线连接。人工智能工具，特别是人工智能系统和机器学习（ML）算法，已在文献中广泛应用，以开发无线 CPS/IoT 的有效方案。本文回顾了人工智能在 CPS 和物联网无线网络中的作用。我们特别关注 ML 范式，例如迁移学习 (TL)、分布式学习和联邦学习，这些范式已发展成为利用大数据在 CPS 和物联网系统中进行学习、适应和预测的构建块。无线网络。此外，我们还强调了当前和未来无线网络面临的与 CPS/IoT 有关的挑战，这些挑战尚未得到解决。