An Energy-Efficient Hybrid Hierarchical Clustering Algorithm for Wireless Sensor Devices in IoT

Chouhan, Nitesh; Jain, Satbir

doi:10.1007/978-3-030-81462-5_1

Cited by 2 publications

(6 citation statements)

References 19 publications

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“…Compute set of party leaders and constituency winners using equation (35) Re-compute and save fitness of each member…”

Section: Multipath Routing Using Tsgwomentioning

confidence: 99%

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Taylor political optimizer‐based cluster head selection in IOT‐assisted WSN networks

Chouhan,

Kumar,

Kumar

2024

Int J Communication

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SummaryIoT‐assisted WSN contains various nodes, which are placed on a huge scale that increases complications. Thus, the challenges and issues of these networks fluctuate as compared to WSNs. Hence, sensor nodes are imperative unit that runs on less energy resources. Hence, devising a robust and energy‐effective protocol for increasing network lifetime is a complex task. This paper devises a novel hybrid optimization‐driven approach for selecting cluster head (CH) in IoT‐assisted WSNs. Initially, the simulation of IoT nodes is done by configuration. Thereafter, the Cluster Head selection is done using a newly devised optimization technique, namely the Taylor‐Political optimizer (Taylor‐PO). Thus, fitness is newly developed by adapting certain attributes like energy, delay, inter and intra‐cluster distance, Link Lifetime (LLT), predicted energy, and delay. Here, the multipath routing is accomplished using Tunicate Swarm gray wolf optimization (TSGWO). Thus, the proposed Taylor‐PO is offered for effective Cluster Head selection along with multipath routing using TSGWO. The proposed Taylor‐PO offered improved performance with smallest delay of 0.006 sec, highest energy of 2.368 J, highest throughput of 494.043 kbps.

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“…Compute set of party leaders and constituency winners using equation (35) Re-compute and save fitness of each member…”

Section: Multipath Routing Using Tsgwomentioning

confidence: 99%

“…Also, for 100 rounds, the energy evaluated by MEQSA-OLSRv2 is 1.080 J, EOMR is 0.399 J, EMEER is 0.434 J, FGGWO is 0.437 J, TSGWO is 1.719 J, and developed Taylor-PO is 2.126 J. [33][34][35] The assessment of techniques using throughput is depicted in Figure 7c). For 10 rounds, the throughput evaluated by MEQSA-OLSRv2 is 170.…”

Section: Assessment With 100 Nodesmentioning

confidence: 99%

Taylor political optimizer‐based cluster head selection in IOT‐assisted WSN networks

Chouhan,

Kumar,

Kumar

2024

Int J Communication

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“…The authors fed the network with a number of energy‐harvesting nodes that act as CHs to limit the premature death of CHs. Chouhan and Jain 113 introduced a hybrid energy‐efficient clustering algorithm that combines the proprieties of static and dynamic clustering. The CH selection is performed using the FCM algorithm and the shortest path is discovered based on a proposed reliable cluster‐based energy‐aware routing (RCER) protocol.…”

Section: Solutionsmentioning

confidence: 99%

“…‐ 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%

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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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An Energy-Efficient Hybrid Hierarchical Clustering Algorithm for Wireless Sensor Devices in IoT

Cited by 2 publications

References 19 publications

Taylor political optimizer‐based cluster head selection in IOT‐assisted WSN networks

Taylor political optimizer‐based cluster head selection in IOT‐assisted WSN networks

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

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