Dynamic Layered Dual-Cluster Heads Routing Algorithm Based on Krill Herd Optimization in UWSNs

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Underwater wireless sensor networks (UWSNs) face challenges with limited node energy and high cluster head loads in traditional routing algorithms.
• Existing clustering routing algorithms often struggle with energy efficiency and network longevity due to node energy constraints.

Purpose of the Study:

• To propose a dynamic layered dual-cluster routing algorithm for UWSNs that addresses energy limitations and cluster head load.
• To enhance the efficiency and lifespan of underwater wireless sensor networks through optimized routing.

Main Methods:

• A dynamic layered dual-cluster routing algorithm is proposed, incorporating Krill Herd optimization.
• Cluster size is determined by node-to-sink distance, and a dynamic layering mechanism prevents repeated cluster head selection.
• The Krill Herd optimization algorithm and its Lagrange model are utilized to select optimal cluster heads and guide node selection.

Main Results:

• The proposed algorithm effectively reduces energy consumption within clusters.
• It achieves a balanced energy distribution across the network, preventing premature node failure.
• Simulation results demonstrate a significant prolongation of the overall network lifetime.

Conclusions:

• The dynamic layered dual-cluster routing algorithm based on Krill Herd optimization offers a viable solution for energy-efficient routing in UWSNs.
• This approach enhances network performance by balancing energy consumption and extending operational duration.
• The method effectively manages cluster head load and optimizes data collection and transition in underwater environments.