HDLCA: hunger driven lion clustering algorithm, a novel energy efficient and scalable clustering approach for...

Area of Science:

• Marine Technology
• Computer Science
• Network Engineering

Background:

• Underwater Wireless Sensor Networks (UWSNs) face significant energy constraints due to non-rechargeable power sources.
• Efficient communication protocols are crucial for extending the operational lifespan of UWSNs.
• Existing routing protocols often struggle to optimize energy consumption and network longevity in dynamic underwater environments.

Purpose of the Study:

• To propose a novel energy-efficient routing protocol for UWSNs inspired by animal behavior.
• To enhance network longevity and performance metrics such as residual energy and throughput.
• To introduce the Hunger Driven Lion Clustering Algorithm (HDLCA) for adaptive cluster head selection and routing.

Main Methods:

• Developed the Hunger Driven Lion Clustering Algorithm (HDLCA), a meta-heuristic clustering-based routing protocol.
• Modeled lion hunting and territorial behaviors to inform sensor node dynamics, energy levels, and proximity for cluster formation.
• Evaluated HDLCA using key performance indicators: residual energy, dead node count, first/last node death, and throughput.

Main Results:

• HDLCA demonstrated significant improvements in network longevity compared to EERBLC (14.37%), EECMR (23.3%), LEACH (59.91%), and K-Means Clustering (34.04%).
• The algorithm effectively optimizes residual energy, minimizes dead nodes, and delays node death.
• HDLCA exhibits strong scalability, resilience to noise, and consistent throughput in simulations.

Conclusions:

• HDLCA offers a robust and energy-efficient solution for underwater wireless sensor networks.
• The bio-inspired approach effectively addresses the critical energy challenges in UWSNs.
• HDLCA provides a promising alternative for enhancing the performance and lifespan of underwater communication systems.