The Internet of Things (IoT) has evolved from a novel technology to an integral part of our everyday lives. It encompasses a multitude of heterogeneous devices that collect valuable data through various sensors. The sheer volume of these interconnected devices poses significant challenges as IoT provides complex network services with diverse requirements on a shared infrastructure. Network softwarization could help address these issues as it has emerged as a paradigm that enhances traditional networking by decoupling hardware from software and leveraging enabling technologies such as Software Defined Networking (SDN) and Network Function Virtualization (NFV). In networking, Machine Learning (ML) has demonstrated impressive results across multiple domains. By smoothly integrating with network softwarization, ML plays a pivotal role in building efficient and intelligent IoT networks. This paper explores the fundamentals of IoT, network softwarization, and ML, while reviewing the latest advances in ML-enabled network softwarization for IoT.

翻译：物联网（IoT）已从一项新兴技术演变为我们日常生活的组成部分。它涵盖了大量异构设备，这些设备通过多种传感器收集宝贵数据。由于物联网在共享基础设施上提供具有多样化需求的复杂网络服务，互连设备的庞大规模带来了巨大挑战。网络软化通过将硬件与软件解耦，并利用软件定义网络（SDN）和网络功能虚拟化（NFV）等使能技术，成为一种增强传统网络的新型范式，有助于解决上述问题。在网络领域，机器学习（ML）已在多个领域展现出令人瞩目的成果。通过与网络软化的平滑集成，ML在构建高效、智能的物联网网络中发挥着关键作用。本文探讨了物联网、网络软化及机器学习的基本原理，同时综述了面向物联网的机器学习赋能网络软化领域的最新进展。