Enabling Architecture for Distributed Intelligent Network Softwarization for the Internet of Things
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929237738389504 |
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| author | Zormati, Mohamed Ali Lakhlef, Hicham |
| author_facet | Zormati, Mohamed Ali Lakhlef, Hicham |
| contents | The Internet of Things (IoT) is becoming a part of everyday life through its various sensing devices that collect valuable information. The huge number of interconnected heterogeneous IoT devices poses immense challenges, and network softwarization techniques are an adequate solution to these concerns. Software Defined Networking (SDN) and Network Function Virtualization (NFV) are two key softwarization techniques that enable the realization of efficient, agile IoT networks, especially when combined with Machine Learning (ML), mainly Federated Learning (FL). Unfortunately, existing solutions do not take advantage of such a combination to strengthen IoT networks in terms of efficiency and scalability. In this paper, we propose a novel architecture to achieve distributed intelligent network softwarization for IoT, in which SDN, NFV, and ML combine forces to enhance IoT constrained networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_05259 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enabling Architecture for Distributed Intelligent Network Softwarization for the Internet of Things Zormati, Mohamed Ali Lakhlef, Hicham Networking and Internet Architecture The Internet of Things (IoT) is becoming a part of everyday life through its various sensing devices that collect valuable information. The huge number of interconnected heterogeneous IoT devices poses immense challenges, and network softwarization techniques are an adequate solution to these concerns. Software Defined Networking (SDN) and Network Function Virtualization (NFV) are two key softwarization techniques that enable the realization of efficient, agile IoT networks, especially when combined with Machine Learning (ML), mainly Federated Learning (FL). Unfortunately, existing solutions do not take advantage of such a combination to strengthen IoT networks in terms of efficiency and scalability. In this paper, we propose a novel architecture to achieve distributed intelligent network softwarization for IoT, in which SDN, NFV, and ML combine forces to enhance IoT constrained networks. |
| title | Enabling Architecture for Distributed Intelligent Network Softwarization for the Internet of Things |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2402.05259 |