An Efficient Performance of Enhanced Bellman-Ford Algorithm in Wireless Sensor Network Using K-Medoid Clustering

Sharma, Govind C.; Kumar, Praveen; Shrivastava, Laxmi

doi:10.1007/978-3-030-44758-8_6

Cited by 1 publication

(3 citation statements)

References 9 publications

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“…‐ Most of K‐medoids‐based solutions consider energy efficiency 74–77,79 . Meanwhile, the work in Gupta et al 75 effectively mitigates the hotspot problem.…”

Section: Solutionsmentioning

confidence: 99%

“…-Most of K-medoids-based solutions consider energy efficiency. [74][75][76][77]79 Meanwhile, the work in Gupta et al 75 effectively mitigates the hotspot problem. The dynamic nature of the network is well managed by previous studies.…”

Section: Node Clusteringmentioning

confidence: 99%

“…Sharma et al 74 presented an efficient clustering scheme for IoS using an enhanced Bellman–Ford algorithm and K‐medoids technique. The Bellman–Ford algorithm was used as the shortest path algorithm.…”

Section: Solutionsmentioning

confidence: 99%

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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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“…‐ Most of K‐medoids‐based solutions consider energy efficiency 74–77,79 . Meanwhile, the work in Gupta et al 75 effectively mitigates the hotspot problem.…”

Section: Solutionsmentioning

confidence: 99%