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Peer-to-Peer Networking and Applications
Erschienen in:

08.03.2024

Cluster based hybrid optimization and kronecker gradient factored approximate optimum path curvature network for energy efficiency routing in WSN

verfasst von: S. Syed Jamaesha, R. Sarath Kumar, M. S. Gowtham

Erschienen in: Peer-to-Peer Networking and Applications | Ausgabe 3/2024

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Abstract

Der Artikel stellt einen neuen Ansatz für energieeffizientes Routing in drahtlosen Sensornetzwerken (WSNs) vor, indem der Hybrid Artificial Tasmanian Devil Humming Bird Algorithm (Hyb-ATDHB) zur Auswahl des Clusterkopfes und das Kronecker Gradient Factored Approximate Path Curvature Network (KFAON) zur optimalen Routenauswahl integriert werden. Der Hyb-ATDHB-Algorithmus nutzt multiobjektive Fitnessfunktionen, um die besten Clusterköpfe basierend auf Knotendichte, Durchsatz und Restenergie zu ermitteln. Die KFAON-Methode nutzt einen Optimierungsansatz zweiter Ordnung, um die effizienteste Route auszuwählen, Verzögerungen zu minimieren und die Paketzustellraten zu erhöhen. Die vorgeschlagene Methode zielt darauf ab, die Lebensdauer des Netzwerks zu verlängern und die Gesamtleistung durch Verringerung des Energieverbrauchs und Optimierung der Datenübertragungswege zu verbessern. Der Artikel bietet außerdem einen detaillierten Simulationsaufbau und eine vergleichende Analyse mit bestehenden Methoden, die die Überlegenheit des vorgeschlagenen Ansatzes hinsichtlich Netzwerklebensdauer, Paketzustellverhältnis, Verzögerung und Energieverbrauch aufzeigen.
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Metadaten
Titel
Cluster based hybrid optimization and kronecker gradient factored approximate optimum path curvature network for energy efficiency routing in WSN
verfasst von
S. Syed Jamaesha
R. Sarath Kumar
M. S. Gowtham
Publikationsdatum
08.03.2024
Verlag
Springer US
Erschienen in
Peer-to-Peer Networking and Applications / Ausgabe 3/2024
Print ISSN: 1936-6442
Elektronische ISSN: 1936-6450
DOI
https://doi.org/10.1007/s12083-024-01675-1

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