A Decentralized Fuzzy C-Means Minimal Clustering Protocol for Energy Efficient Wireless Sensor Network

Thakurta, Parag Kumar Guha; Roy, Soumali

doi:10.1109/pdgc.2018.8745853

Cited by 1 publication

(3 citation statements)

References 8 publications

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“…‐ Most of the FCM‐based solutions consider energy efficiency 101‐103,105‐108,112‐114,118,123,124 110,111,115 . In terms of distribution, previous works 102,103,106 are effective.…”

Section: Solutionsmentioning

confidence: 99%

“…-Most of the FCM-based solutions consider energy efficiency. [101][102][103][105][106][107][108][112][113][114]118,123,124110,111,115 In terms of distribution, previous works 102,103,106 are effective. Previous studies 111,112,118,121 provide load-balancing.…”

Section: Node Clusteringmentioning

confidence: 99%

“…Thakurta and Roy 101 presented an FCM energy‐efficient clustering protocol for IoS networks. Under an unequal distribution of nodes, the cluster formation is characterized as a fuzzy partition of sample space.…”

Section: Solutionsmentioning

confidence: 99%

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

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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%