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In recent years, underwater acoustic networks (UANs) include routing protocols to ensure acoustic signal propagation. Here, underwater communication networks balance transmission power with limited bandwidth and high latency. Hence, this research proposes the non-dominated sorting genetic algorithm-2 (NSGA-2) to handle optimization problems effectively with improved quality and channel conditions. Initially, Marine Autonomous Robotics for Localization and INavigation (MARLIN) project dataset is used for UAN vehicles and drones. Then, preprocessing is done by using Hilbert–Huang transform (HHT) and interquartile range (IQR) for data filtering and noise reduction which is focused on relevant frequency bands. For feature extraction, independent component analysis (ICA) is used for filtering relevant data particularly to address signal separation. Finally, the experimental results demonstrate that the proposed NSGA-2 approach attains packet delivery ratio (PDR) of (97.98%) and throughput of (96.87kbps) when compared to existing multi-objective adaptive evolutionary algorithm based on reference sets (MAEARSs).

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Routing and Power Control in Underwater Acoustic Networks Using Non-dominated Sorting Genetic Algorithm-2

  • 会议论文

  • 发表日期：

    2026年

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  • Amaar Haamid Shnaeen,
  • G. V. S. Padma Rao

摘要

In recent years, underwater acoustic networks (UANs) include routing protocols to ensure acoustic signal propagation. Here, underwater communication networks balance transmission power with limited bandwidth and high latency. Hence, this research proposes the non-dominated sorting genetic algorithm-2 (NSGA-2) to handle optimization problems effectively with improved quality and channel conditions. Initially, Marine Autonomous Robotics for Localization and INavigation (MARLIN) project dataset is used for UAN vehicles and drones. Then, preprocessing is done by using Hilbert–Huang transform (HHT) and interquartile range (IQR) for data filtering and noise reduction which is focused on relevant frequency bands. For feature extraction, independent component analysis (ICA) is used for filtering relevant data particularly to address signal separation. Finally, the experimental results demonstrate that the proposed NSGA-2 approach attains packet delivery ratio (PDR) of (97.98%) and throughput of (96.87kbps) when compared to existing multi-objective adaptive evolutionary algorithm based on reference sets (MAEARSs).