Double Deep Reinforcement Learning for Optimization of Underwater Acoustic Sensor Network Energy Consumption and Congestion Control

ABSTRACT Underwater Acoustic Sensor Networks (UASNs) are essential for offshore engineering, military surveillance, environmental monitoring, and oceanographic exploration. However, challenges including high latency, congested networks, low bandwidth, and energy limitations make it difficult to communicate effectively in UASNs. In order to tackle these problems, this study suggests a new Energy‐Efficient Double‐Level Deep Reinforcement Learning with Chaotic Chimp Optimization (E2D2RL‐ChCo) model that is intended to improve localization precision, reduce packet loss, and effectively handle network congestion. The proposed E2D2RL‐ChCo model uses transformer‐based Markov Decision Processes (MDPs) for node localization at the top level and congestion‐aware routing at the bottom level to guarantee efficient task scheduling and energy‐efficient data transfer. By integrating Chaotic Chimp Optimization (ChCo), learning parameters are adjusted to enhance convergence and solution quality. Simulation results demonstrate up to 33.8% reduction in energy consumption, 61.5% decrease in end‐to‐end delay, and over 52% improvement in successful packet delivery compared to baseline models. The proposed model also improves localization error to 0.0075, outperforming existing strategies. This work fills gaps in existing literature by integrating deep reinforcement learning and metaheuristics to optimize both localization and congestion in real‐time UASN scenarios. This approach enables more sustainable and reliable UASN operations, paving the way for enhanced performance in real‐world aquatic environments.