HDLCA: hunger driven lion clustering algorithm, a novel energy efficient and scalable clustering approach for underwater wireless sensor nodes

Underwater Wireless Sensor Networks (UWSNs), a subset of traditional WSNs, face critical challenges due to their reliance on non-rechargeable, irreplaceable power sources, making energy-efficient communication essential. This paper proposes a novel meta-heuristic clustering-based routing protocol in...

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Vydáno v:Scientific reports Ročník 15; číslo 1; s. 33292 - 27
Hlavní autoři: Raghu, Shyamsundar, Shankar, T.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 26.09.2025
Nature Publishing Group
Nature Portfolio
Témata:
639/166
639/166/987
Acoustics
Algorithms
Clustering
Communication
Energy consumption
Energy efficiency
Humanities and Social Sciences
Hunger
Mathematical models
Meta heuristic clustering
Modems
multidisciplinary
Network topology
Routing
Science
Science (multidisciplinary)
Sensors
Software
Territorial behavior
Underwater
Underwater wireless sensor networks
Routing
Network topology
Underwater wireless sensor networks
Meta heuristic clustering
ISSN:2045-2322, 2045-2322
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Shrnutí:Underwater Wireless Sensor Networks (UWSNs), a subset of traditional WSNs, face critical challenges due to their reliance on non-rechargeable, irreplaceable power sources, making energy-efficient communication essential. This paper proposes a novel meta-heuristic clustering-based routing protocol inspired by the hunger-driven hunting and territorial behaviour of lions, termed the Hunger Driven Lion Clustering Algorithm (HDLCA). Unlike other approaches, HDLCA directly maps lion behaviour to sensor node dynamics, enabling adaptive cluster head selection and efficient sub-cluster formation based on energy levels and node proximity. The algorithm is evaluated using key performance metrics including residual energy, dead node count per round, first and last node death, and throughput. Simulation results show that HDLCA optimizes these metrics effectively compared to EERBLC, EECMR, LEACH, and K-Means Clustering. Specifically, HDLCA achieves improvements in network longevity by 23.3%, 14.37%, 34.04%, and 59.91% when compared to EECMR, EERBLC, K-Means Clustering, and LEACH respectively. Additionally, HDLCA exhibits strong scalability, noise resilience, and consistent throughput, making it a robust and efficient solution for underwater deployments.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-18043-5