An Overview of Machine Learning-Enabled Network Softwarization for the Internet of Things
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916118235447296 |
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| author | Zormati, Mohamed Ali Lakhlef, Hicham |
| author_facet | Zormati, Mohamed Ali Lakhlef, Hicham |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_05255 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Overview of Machine Learning-Enabled Network Softwarization for the Internet of Things Zormati, Mohamed Ali Lakhlef, Hicham Networking and Internet Architecture 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. |
| title | An Overview of Machine Learning-Enabled Network Softwarization for the Internet of Things |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2402.05255 |