## 1 Introduction
In response, acoustic modems have emerged asa practical, low-energy solution, enabling broader and more sustainable deployment in underwater environments. An effective MAC protocol is essential for regulating access to the communication medium in UWANs. It must ensure equitable resource allocation among nodes while coping with slow propagation, fluctuating delays, bandwidth constraints, and high energy costs. Environmental noise, shifting topologies, and intermittent connectivity further complicate protocol design. Thus, MAC protocols for UACNs must be specifically crafted to handle these demanding conditions. Given the defining traits of UASNs--such as long transmission delays, constrained energy reserves, low data rates, high BER, and dynamic node arrangements--each network layer must adopt purpose-built protocols. The MAC layer, in particular, plays a critical role in maintaining system efficiency and extending network lifetime, empowering sensor nodes to carry out their roles effectively under challenging underwater conditions [23]. The MAC protocols can efficiently schedule the use of resources within the network and avoid data collision [24]. And the mobile nodes in UACNs, such as, for example, Autonomous Underwater Vehicles (AUVs), Unmanned Underwater Vehicles (UUVs), and free-floaters, are essential for tracking underwater activities and transmitting information about the environment with the help of acoustic channels. Table 1 provides a comparative overview of communication technologies, highlighting the existing alternatives to acoustic communication channels, and it also clearly illustrates why acoustic channels are considered a high priority. The use of acoustic channels enables data collaboration to take place close to the network or even remote data centers. Mobile UWASNs have mobile sensor nodes that can provide cost- and energy-efficient data gathering and monitoring, as well as greater resiliency against node failures, allowing them to be used in short term missions, such as target detection, oil spill monitoring, and underwater surveillance. The information gained from the data collected assists in making decisions by allowing for information to be analyzed and integrated into decisions taken [19; 20]. The mobility of nodes adds an extra layer of complexity to MAC design as it necessitates adjustments to handle dynamic node locations and to promote resilience in the face of rapidly changing environmental conditions. These networks are a collection of sensor nodes deployed in underwater environments that can be used to gather essential information about changes in oceanography [25]. Recently, reinforcement learning (RL) has been identified as a feasible approach to develop UWASN-MAC protocols at this abstraction level while keeping communication cost low [21]. The proposed protocols implement all  
Figure 1: UWSN deployment scenario with sensor nodes and data flow paths. sensor nodes as RL agents, which gives the agents the ability to sequentially refine the selection of good transmission strategies by continuous interaction with the environment. Many protocols employing various optimization methods, especially novel ML algorithms, have been suggested for the improvement of the energy efficiency in UWSNs. In this regard, one of these protocols, TDTSPC-MAC, address time synchronization, power control, clustering, and sleep mechanisms to minimize the energy utilization in 3D UWSNs [13]. UWSN deployment scenarios with sensor nodes and data flow paths are shown in Figure 1. In [26], the energy-efficient routing method in UWSNs is proposed to enhance the overall energy utilization, circumventing the limited energy source issue. Throughput, which is also enhanced in the proposed framework, is another important evaluation criterion of transmission performance.