Underwater wireless sensor network-based multihop data transmission using hybrid cat cheetah optimization algorithm.

Area of Science:

• Marine technology
• Wireless sensor networks
• Optimization algorithms

Background:

• Underwater wireless sensor networks (UWSNs) are crucial for oceanic monitoring but face data transmission challenges due to the aquatic environment.
• Existing routing protocols struggle with energy efficiency and network longevity in UWSNs.

Purpose of the Study:

• To propose a novel Hybrid Cat Cheetah Optimization Algorithm (HC²OA) for energy-efficient clustering-based routing in UWSNs.
• To optimize cluster head (CH) selection and multi-hop data transmission from sensor nodes to sink nodes (SN).

Main Methods:

• The HC²OA algorithm partitions the network into clusters, with CHs managing data collection from sub-clusters (CMs).
• CH selection is optimized based on distance and residual energy, employing multi-hop routing for data transmission to SNs.
• Simulations were conducted using the NS2 simulator to evaluate the algorithm's performance.

Main Results:

• The proposed HC²OA demonstrates significant improvements over existing methods in network lifetime, packet delivery ratio, and energy consumption.
• Achieved an energy consumption of 0.2 J and a packet delivery ratio of 95%.
• The network lifetime extends to approximately 60 hours for a coverage area of 14 km.

Conclusions:

• HC²OA effectively mitigates complexities in multi-hop routing and CH selection for UWSNs.
• The algorithm offers a superior solution for energy-efficient data transmission, enhancing the overall performance and longevity of underwater monitoring systems.