## 2 Literature Review  
The existing literature on MAC and routing protocols for UWSNs addresses challenges like long propagation delays, low data rates, high energy consumption, and collision management. Early works, such as RIPT [14], introduced receiver-initiated protocols to enhance throughput and reduce collisions. Handshaking-based methods [15] improved channel utilization through synchronization but faced issues with synchronization precision and latency. Enhanced Aloha variants [16] tackled throughput limitations but struggled in dense networks due to persistent collision risks. More recent studies have leveraged reinforcement learning. For example, Q-learning-based multi-hop MAC protocols [17], DR-D3 QN-MA for time-slot optimization [18], and MR-SFAMA-Q for multi-receiver handshake optimization [19] have shown adaptability and efficiency. However, challenges with real-world validation, scalability, and energy efficiency remain. A MACA-based energy-efficient protocol using Q-learning [20] and a Q-learning-enhanced framed Aloha approach [21] demonstrated improved throughput and delay management but still faced limitations in multi-hop energy efficiency and collision avoidance. Despite these advancements, many protocols lack the integration of a DQN-based MAC-routing approach. This integration is necessary for achieving robust multi-hop underwater networks. DQN-based frameworks typically focus on hybrid protocols that incorporate adaptive learning techniques to optimize scalability, reduce computational complexity, and enhance real-world applicability. However, conventional TDMA-based scheduling [22] mitigated collision rates but led to inefficiencies under varying traffic conditions due to rigid slot allocation. Traffic-aware MAC protocols [23] improved fairness but lacked routing integration, while reinforcement learning-based MAC schemes for multimedia [24] optimized throughput but increased computational complexity. Dynamic slot scheduling [25] enhanced adaptability but raised processing overhead. Contention-free MAC [26] prioritized critical data but neglected low-priority traffic, and spatially fair MAC [27] improved network stability but lacked dynamic adaptation. EE-UWSNs have integrated MAC and routing for energy efficiency [28], yet real-world validation remains limited. Recent reinforcement learning-based strategies [29] have enhanced scalability and adaptability but require optimization to reduce computational overhead. While these advancements improve UWSN performance, challenges persist in scalability, multi-hop integration, and real-world adaptability. This highlights the need for hybrid approaches that seamlessly integrate DQN based optimization of MAC and routing layers while leveraging advanced learning techniques for improved efficiency and robustness. The summary of the of Literature Review on MAC Protocols for UWSNs are given in Table 1.  
In addition, the authors of [30; 31; 32; 33] highlight recent advances in maritime computing and communication, with special attention to efficiency, privacy, and resource minimization. Specifically, ref. [30] proposes a space-air-ground-sea integrated network, mobile edge computing, and deep reinforcement learning to minimize communication and computation resources, with improved efficiency. In contrast, ref. [31] condemns centralized machine learning for sea use due to bandwidth, energy, and privacy constraints and instead advocates the federated strategy (FedShip) using over-the-air computing (AirComp) to improve energy and spectrum efficiency. However, redundancy is present in the third and fourth of the last three papers on parallel conclusions regarding the superiority of FedShip over CML, namely fuel consumption prediction and data privacy [31; 32; 33]. Although such studies