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<p>Underwater wireless sensor networks (UWSNs) play a vital role in facilitating communication and monitoring within underwater environments. However, these networks face significant challenges due to harsh environmental conditions and limited energy resources. To enhance their performance, key factors such as cover- age rate, energy efficiency, and network longevity must be optimized. This study introduces a jellyfish algorithm that integrates both static and mobile sensor deployment strategies to address these challenges. The algorithm’s effectiveness is evaluated through simulation, focusing on its impact on network coverage, energy consumption, and operational lifespan. Results show that the proposed algorithm significantly reduces energy usage to below 500&#xa0;J, in contrast to over 630&#xa0;J consumed by existing methods. It also extends the network lifetime to approximately 2.2 × 10<sup>14</sup> h, outperforming FOA-BOA and NSGA-II, which achieve lifespans of 1.8 × 10<sup>14</sup> h and 1.6 × 10<sup>14</sup> h, respectively. In terms of coverage, the proposed method achieves 99%, compared to just 29% and 27% for FOA- BOA and NSGA-II. The integration of mobile deployment techniques enhances the balance between energy efficiency and coverage, leading to improved net- work performance. Overall, the proposed approach offers a promising solution for increasing the sustainability and efficiency of UWSNs, making them better suited for long&#xa0;term, energy&#xa0;constrained underwater monitoring applications.</p>

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The jellyfish algorithm: a new approach for enhancing coverage, reducing energy consumption, and extending network lifetime in underwater wireless sensor networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月22日

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  • Kadoke Marco Kadoke,
  • Sonia Goyal,
  • Ranjit Kaur

摘要

Underwater wireless sensor networks (UWSNs) play a vital role in facilitating communication and monitoring within underwater environments. However, these networks face significant challenges due to harsh environmental conditions and limited energy resources. To enhance their performance, key factors such as cover- age rate, energy efficiency, and network longevity must be optimized. This study introduces a jellyfish algorithm that integrates both static and mobile sensor deployment strategies to address these challenges. The algorithm’s effectiveness is evaluated through simulation, focusing on its impact on network coverage, energy consumption, and operational lifespan. Results show that the proposed algorithm significantly reduces energy usage to below 500 J, in contrast to over 630 J consumed by existing methods. It also extends the network lifetime to approximately 2.2 × 1014 h, outperforming FOA-BOA and NSGA-II, which achieve lifespans of 1.8 × 1014 h and 1.6 × 1014 h, respectively. In terms of coverage, the proposed method achieves 99%, compared to just 29% and 27% for FOA- BOA and NSGA-II. The integration of mobile deployment techniques enhances the balance between energy efficiency and coverage, leading to improved net- work performance. Overall, the proposed approach offers a promising solution for increasing the sustainability and efficiency of UWSNs, making them better suited for long term, energy constrained underwater monitoring applications.