Authors: Mohamed Ali Zormati, Hicham Lakhlef, Sofiane Ouni

Published on: February 07, 2024

Impact Score: 8.3

Arxiv code: Arxiv:2402.05270

Summary

• What is new: This paper investigates the integration of Machine Learning (ML) with network softwarization (including Software Defined Networking and Network Function Virtualization) in the IoT ecosystem, a relatively unexplored approach.
• Why this is important: The burgeoning number of IoT devices creates diverse and complex service requirements on common network infrastructures, posing significant challenges.
• What the research proposes: The paper proposes leveraging ML within a network softwarization framework to enable smarter, efficient, and self-adaptive IoT networks.
• Results: A comprehensive overview of IoT, network softwarization, and ML integration is presented, identifying promising research directions for enhanced IoT networks.

Technical Details

Technological frameworks used: Network Softwarization (Software Defined Networking, Network Function Virtualization)

Models used: Machine Learning algorithms

Data used: nan

Potential Impact

Telecommunications, smart home providers, smart city technology firms, automotive companies with smart vehicle technologies, and IoT device manufacturers.

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