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 |
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26.09.2025
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| Abstract | 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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| AbstractList | 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. 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.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. Abstract 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. |
| ArticleNumber | 33292 |
| Author | Raghu, Shyamsundar Shankar, T. |
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| SubjectTerms | 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 |
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| Title | HDLCA: hunger driven lion clustering algorithm, a novel energy efficient and scalable clustering approach for underwater wireless sensor nodes |
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