## 2 Related Work
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Technology** & **Acoustic Communication** & **Optical Communication** & **Electromagnetic (EM) Waves** \\ \hline
**Range** & Long-range (up to several kilometers) & Short-range (typically \(<\) 100 m) & Very short-range (a few meters) \\
**Data Rate** & Low-to-moderate (kbps range) & High (Mbps to Gbps) & Moderate (Mbps range) but severely limited underwater \\
**Latency** & High (slow propagation speed of sound, 1500 m/s) & Low (speed of light) & Low (speed of light) \\
**Energy Efficiency** & Moderate (energy-intensive for long-range) & High for short-range but energy-intensive for high-power systems & Low (high attenuation requires excessive power) \\
**Attenuation** & Low in water, but affected by multipath fading and noise & High (scattering/absorption by water particles) & Extremely high (sewater is highly conductive) \\
**Environmental Impact** & Affected by temperature, salinity, pressure, and noise & Sensitive to water turbidity and suspended particles & Minimal impact but impractical underwater \\
**Cost** & Moderate (acoustic modems are affordable) & High (requires precise alignment) & Low (but ineffective underwater) \\
**Applications** & Long-range UWSNs (e.g., ocean monitoring) & Short-range tasks (e.g., AUV docking) & Rarely used underwater \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of underwater wireless communication technologies. access, facilitating theoretical evaluation of the throughput under multichannel collision scenarios. In contrast, OA-CMAC [36] integrates optical and acoustic techniques to achieve high-speed, real-time data transmission. Through using optical links, it extends node lifetime by nearly 30% compared to conventional acoustic communication, and it enhances data delivery using a combination of CSMA/CA and SDMA-derived methods. Acoustic signaling is used for positioning, while optical handshakes validate channel availability. In unstable optical conditions, high-priority messages are automatically rerouted via acoustic links, ensuring data delivery continuity. Meanwhile, the MAC framework proposed in [37] adopts a multi-level quorum system for scheduling collision-free time slots in high-load networks. By dynamically assigning non-overlapping slots to conflicting nodes, it enhances spatial reuse and ensures fairness via data-driven allocation logic. A lightweight retransmission technique is embedded to minimize delay effects, bolstering overall reliability and energy performance. On the other hand, OCMAC [29] emphasizes synchronous duty-cycled communication. Its performance was modeled through a Markov chain, demonstrating significant energy savings alongside acceptable levels of throughput and reliability. The protocol leverages RTS-based scheduling to curb collisions and boost communication efficiency. RA-MAC [28] introduces a relay-assisted architecture to improve energy efficiency by offloading direct transmissions to nearby relay nodes. It adopts a random access method with feedback while keeping control overhead minimal. Relay counts can be increased to enhance energy savings without significantly affecting throughput. The embedded feedback loop continually refines forwarding decisions, optimizing both performance and resource usage. In the context of UWSNs carrying multimedia data, a reinforcement learning-driven MAC protocol [33] was proposed to optimize both energy consumption and transmission reliability. The protocol follows a two-phase approach: it first adjusts sensing time slots using RL algorithms, and it then adaptively tunes the contention window through network feedback. A Q-learning framework is applied in the absence of beacon or relay support to enable collision-free communication. A TXOP-based method is further employed to improve multi-hop transmission efficiency while limiting the need for frequent acknowledgments. The CA-MAC protocol [31] was designed to tackle long delays, low data rates, and high BER in UWSNs.