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Background <p>Underwater Acoustic Sensor Networks (UASNs) necessitate effective cluster head (CH) selection to enhance energy efficiency, prolong network longevity, and augment data transfer. Conventional methods frequently encounter energy inefficiencies, resulting in diminished network performance.</p> Objective <p>This study presents a new method called the Hybrid Tunicate-Whale Optimisation Algorithm (HTWOA), which is based on natural processes to improve CH selection in UASNs. The goal is to make power use and network speed better by sorting nodes according to how much energy they use.</p> Methods <p>HTWOA leverages the Tunicate Swarm Algorithm (TSA) to identify nodes as low-, intermediate-, or high-power users. The suggested technique was assessed using 120-node simulations in a 1500&#xa0;m<sup>3</sup> 3D underwater environment. Performance parameters, including power consumption, packet delivery ratio, latency, and network lifespan, were tested to Depth-based Routing (DR), Power Conserving Depth-based Routing (PCDR), and Power Conserving Layered Cluster Head Rotation (PCLCHR).</p> Results <p>The HTWOA protocol achieved a 30% decrease in power usage compared to conventional approaches. With a 100% increase in node count, the packet delivery ratio exhibited a small improvement. Additionally, the end-to-end latency dropped by about 20%, while network lifespan increased by up to 25%.</p> Conclusion <p>The proposed HTWOA-based protocol considerably increases CH selection in UASNs, leading to better energy efficiency, decreased latency, and prolonged network longevity. These findings imply that HTWOA is an excellent approach for optimizing UASN performance and ensuring efficient data transmission.</p>

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Hybrid Bio Inspire Algorithm used for Optimum Path Communication and Improved Power Conserving Routing Protocol Design for Underwater Acoustic Sensor Network

  • 期刊论文

  • 发表日期：

    2025年2月10日

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  • A. Kannappan,
  • Nagesh Kolagani

摘要

Background

Underwater Acoustic Sensor Networks (UASNs) necessitate effective cluster head (CH) selection to enhance energy efficiency, prolong network longevity, and augment data transfer. Conventional methods frequently encounter energy inefficiencies, resulting in diminished network performance.

Objective

This study presents a new method called the Hybrid Tunicate-Whale Optimisation Algorithm (HTWOA), which is based on natural processes to improve CH selection in UASNs. The goal is to make power use and network speed better by sorting nodes according to how much energy they use.

Methods

HTWOA leverages the Tunicate Swarm Algorithm (TSA) to identify nodes as low-, intermediate-, or high-power users. The suggested technique was assessed using 120-node simulations in a 1500 m3 3D underwater environment. Performance parameters, including power consumption, packet delivery ratio, latency, and network lifespan, were tested to Depth-based Routing (DR), Power Conserving Depth-based Routing (PCDR), and Power Conserving Layered Cluster Head Rotation (PCLCHR).

Results

The HTWOA protocol achieved a 30% decrease in power usage compared to conventional approaches. With a 100% increase in node count, the packet delivery ratio exhibited a small improvement. Additionally, the end-to-end latency dropped by about 20%, while network lifespan increased by up to 25%.

Conclusion

The proposed HTWOA-based protocol considerably increases CH selection in UASNs, leading to better energy efficiency, decreased latency, and prolonged network longevity. These findings imply that HTWOA is an excellent approach for optimizing UASN performance and ensuring efficient data transmission.