## 1 Introduction
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Collision Rate** & **Energy Efficiency** & **Throughput** & **Latency** & **Adaptability** \\ \hline \multirow{3}{*}{[21]} & Reduced through Q-learning and multi-hop approach. & Improved by optimizing path selection via Q-learning. & Higher compared to traditional schemes. & Mitigates latency, but not fully optimized. & Adaptive via Q-learning based on real-time data. \\ \hline \multirow{3}{*}{[22]} & Low collision rate through hybrid optimization techniques (COOT-HOA, PSO-ACO). & High energy efficiency through optimized base node selection and long short-term memory network (LSTM) predictions. & High throughput due to optimized data routing and active zone selection. & Moderate latency, optimized via signal parameter estimation (ESPRIT). & Highly adaptable with deep learning LSTM for mobile node prediction. \\ \hline \multirow{3}{*}{Present research} & Significantly lower due to robust efficient design. & Enhanced through robust, more advanced mechanisms. & Achieves even better throughput due to reduced collisions. & Further reduction in latency with robust design of new algorithm. & More adaptable, with robust flexible and dynamic approach. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between different existing MAC protocols and the present research. frequency are high, the sensor nodes are consuming too much power to perform the assigned task [25]. Then, such a UWSN-based communication system is not energy efficient, because charging the battery again and again requires resources, and maybe those resources will increase so much with time that the whole application needs to be shut down because it is not financially sustainable [26; 27]. In this context, to address the above-mentioned issue of energy, in our work we have exploited a Q-learning-based approach to formulate an energy-efficient MAC protocol for UWSNs, with collision prevention. Given this, different research groups have put forward different MAC strategies targeting energy efficiency and communication dependability in underwater networks [28]. MAC protocols are crucial since they control the channel access of the sensor nodes in the UWSNs. Because the acoustic waves are very slow to propagate through water, MAC protocols can take advantage of the available bandwidth as long as future processes conserve power [29]. Due to the use of multiple nodes to perform a specific task for a given application to access the communication medium, contention-based MAC protocols have also proven more efficient underwater. These protocols, particularly the multiple access with collision avoidance (MACA)-based protocols, address the hidden node problem, an underlying cause of collisions in UWSNs [30]. The hidden node problem is where one or more nodes are not within each other's range and do not know of the existence of one another but still send packets to the intermediary node. This causes packets to be sent and collide with each other, which in turn consumes energy. Conventional power control and collision avoidance (CAPC) MAC protocols have been developed to minimize the energy consumption of sensor nodes in UWSNs [31]. These protocols control the transmission power of the wireless devices depending on the data packet that is being transmitted, so that the power consumption is not overstretched. Power control methods are used to minimize energy usage, but these processes worsen the hidden node problem due to a high collision probability [32; 33]. Interference in UWSNs is mainly because of the collision between control and data packets, which is generally termed long interference range collision (LIRC) [34]. The LIRC issue shows that it is challenging to identify potential interference in an underwater environment that suffers from both signaling overhead and propagation delay [35].