A Multi Path Loss Model Using Hybrid Gooseneck Barnacle Nizar Optimization With Normal Mode Theory in Underwater Acoustic Communication

Abstract

Acoustic communication in Underwater Wireless Sensor Networks (UWSNs) faces persistent challenges due to severe bandwidth limitations, multipath propagation, and frequency-dependent attenuation, all of which are further aggravated by sensor mobility driven by water tides and turbulence. These constraints make underwater topology planning and optimal projector–hydrophone placement a highly complex problem. To address these challenges, this study introduces a Hybrid Gooseneck Barnacle Nizar Optimization (HGBNO) algorithm that integrates nonlinear adaptive weighting, a sigmoid-based hybrid operator, and search-diversity–driven exploration–exploitation balancing to accurately determine optimal placement parameters, including distance, depth, reflection rate, and operating frequency. The optimized configuration is further evaluated using environmentally adaptive normal-mode theory, which incorporates mode-dependent sound-speed perturbations to more realistically capture shallow-water acoustic behavior. To validate the model, controlled tank trials at UWARL-SSN were conducted to quantify the influence of projector–hydrophone geometry on transmission loss. Simulation and experimental analyses demonstrate that the proposed framework achieves a minimum transmission loss of −4.58 dB and a maximum of 23.71 dB, outperforming classical optimization methods and conventional Urick-based propagation models. The improved performance is consistent across depths of 2–4 m and ranges of 0–12 m under static channel conditions. These results confirm that the integration of HGBNO-driven parameter optimization with adaptive normal-mode modeling significantly enhances prediction accuracy and robustness, offering a strong foundation for real-world shallow-water acoustic communication and future extension to dynamic underwater environments.