The Internet of Things (IoT) is an emerging technology that aims to connect heterogeneous and constrained objects to each other and to the Internet. It has grown significantly in a wide variety of applications such as smart homes, smart cities, smart vehicles, etc. The huge number of connected devices increases the challenges, as IoT provides diverse and complex network services with different requirements on a common infrastructure. Network Softwarization is the latest network paradigm that transforms traditional network processes to the separation of hardware and software by using some enabling network technologies such as Software Defined Networking (SDN) and Network Function Virtualization (NFV). Machine Learning (ML) plays an essential role in creating smarter IoT networks, as it has shown remarkable results in various domains. Given that the network softwarization allows it to be easily integrated, ML can play a crucial role in efficient and self-adaptive IoT networks. In this paper, we provide a detailed overview of the concepts of IoT, network softwarization, and ML, and we study and discuss the state of the art of intelligent ML-enabled network softwarization for IoT. We also identify the most prominent future research directions to be considered.

翻译：物联网（IoT）是一项新兴技术，旨在将异构且受限的对象相互连接并接入互联网。它在智能家居、智慧城市、智能车辆等广泛应用中取得了显著发展。随着物联网在共同基础设施上提供多样化且复杂的网络服务并具有不同需求，海量连接设备进一步加剧了挑战。网络软化是最新的网络范式，通过采用软件定义网络（SDN）和网络功能虚拟化（NFV）等使能网络技术，将传统网络流程转变为硬件与软件的分离。机器学习（ML）在创建更智能的物联网网络中发挥着关键作用，因为它在多个领域展现了卓越成果。鉴于网络软化便于集成，机器学习可在高效且自适应的物联网网络中扮演关键角色。本文详细概述了物联网、网络软化及机器学习的概念，并研究讨论了面向物联网的智能机器学习驱动的网络软化最新技术现状。我们还指出了未来最值得关注的研究方向。