An Energy Efficient Self Organizing Map Based Clustering Protocol For IoT Networks

Merah, Malha; Aliouat, Zibouda; Kara-Mohamed, Chafia

doi:10.1109/setit54465.2022.9875799

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…The latter impacts the network load and balances the energy consumption. Merah et al 126 designed an energy‐efficient clustering protocol based on the SOM algorithm for IoT networks. Network devices are grouped into clusters using the SOM algorithm, and CHs are selected based on their energy level and distance from the winning neuron.…”

Section: Solutionsmentioning

confidence: 99%

Machine learning‐based clustering protocols for Internet of Things networks: An overview

Merah

Aliouat

Harbi

et al. 2023

Int J Communication

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Summary The Internet of Things (IoT) continues to expand the current Internet, opening the door to a wide range of novel applications. The increasing volume of the IoT requires effective strategies to overcome its challenges. Machine Learning (ML) has led to a growing technology that enables computers to solve problems without the need for knowledge of their intricate details. Over the past years, various ML techniques have been used to efficiently manage IoT networks. Clustering is a technique that has proven its performance in the networking domain. Many works in the literature have studied ML‐based clustering methods for IoT networks, including their main properties, characteristics, underlying technologies, and open issues. In this paper, we focus on topology‐centered ML‐based clustering protocols for IoT networks. Specifically, we investigate the potential benefits of adopting the clustering approach to address several IoT challenges. Moreover, we provide a comprehensive taxonomy of ML‐based clustering algorithms for IoT networks. Finally, we statistically analyze the incorporation of ML techniques for clustering in various IoT systems and highlight the related open issues.

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Section: Solutionsmentioning

confidence: 99%