الگوریتم مسیریابی نوین برای شبکه‌های حسگر بی‌سیم زیر آب با استفاده از یادگیری تقویتی و شبکه‌های عصبی متخاصم زایشی

کرمی بنماران, نوید; طاهری جوان, نستوه

doi:10.22052/scj.2025.255711.1281

الگوریتم مسیریابی نوین برای شبکه‌های حسگر بی‌سیم زیر آب با استفاده از یادگیری تقویتی و شبکه‌های عصبی متخاصم زایشی

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه بین المللی امام خمینی (ره)، قزوین، ایران.

10.22052/scj.2025.255711.1281

چکیده

شبکه‌های حسگر بی‌سیم زیر آب با چالش‌هایی مانند محدودیت انرژی گره‌ها، حرکت حسگرها در اعماق مختلف و نیاز به روش‌های ارتباطی متنوع مواجه هستند. این محدودیت‌ها باعث کاهش کارآیی الگوریتم‌های مسیریابی مرسوم در سایر شبکه‌های بی‌سیم چندگامه در این نوع از شبکه‌ها می‌شوند. در این پژوهش، یک روش مسیریابی نوین مبتنی بر یادگیری تقویتی و شبکه‌های عصبی متخاصم زایشی برای کاربرد در شبکه‌های حسگر بی‌سیم زیر آب معرفی می‌شود. هدف اصلی در این رویکرد، کشف و ذخیره مسیرهای مناسب برای ارسال داده‌ها است. سپس، با استفاده از الگوی شبکه‌های عصبی متخاصم زایشی (GAN)، این مسیرها برای آموزش یک مدل یادگیری عمیق بکار گرفته می‌شوند و سعی بر تولید مسیرهای جدید در این چرخه است. نتایج شبیه‌سازی نشان می‌دهند که استفاده از این رویکرد به بهبود نرخ تحویل موفق بسته‌ها به مقصد و افزایش طول عمر شبکه نسبت به روش‌های یادگیری تقویتی سنتی در شبکه‌های حسگر زیر آب منجر می‌شود.

کلیدواژه‌ها

موضوعات

شبکه بی سیم

عنوان مقاله [English]

A routing algorithm for underwater wireless sensor networks based on reinforcement learning and generative adversarial neural networks

نویسندگان [English]

Navid Karami Benmaran
Nastooh Taheri Javan

Computer Engineering Department, Imam Khomeini International University, Qazvin, Iran

چکیده [English]

Underwater wireless sensor networks (UWSNs) face various constraints such as limited node energy, sensor mobility at different depths, and the need for diverse communication methods. These limitations reduce the efficiency of conventional routing algorithms used in other multi-hop wireless networks. In this study, an innovative routing method based on reinforcement learning and generative adversarial neural networks (GANs) is proposed for UWSNs. The proposed approach aims to discover and store optimal data transmission paths within the network. Subsequently, these paths are used to train a deep learning model following the generative adversarial neural network framework, allowing for the generation of new routes. Simulation results demonstrate that the proposed method improves the successful packet delivery rate and extends network lifetime compared to traditional reinforcement learning techniques in UWSNs.

کلیدواژه‌ها [English]

Underwater wireless sensor network
Routing
Reinforcement learning
Deep learning
Generative adversarial neural network

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