An Overview of Machine Learning-Enabled Network Softwarization for the Internet of Things
<|reference_start|>An Overview of Machine Learning-Enabled Network Softwarization for the Internet of Things: 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.<|reference_end|>
@article{zormati2024an,
    title={An Overview of Machine Learning-Enabled Network Softwarization for the
  Internet of Things},
    author={Mohamed Ali Zormati, Hicham Lakhlef},
    journal={2023 International Conference on Software, Telecommunications and
  Computer Networks (SoftCOM), Split, Croatia, 2023, pp. 1-6},
    year={2024},
    doi={10.23919/SoftCOM58365.2023.10271664},
    archivePrefix={arXiv},
    eprint={2402.05255},
    primaryClass={cs.NI}
}