## 7 Discussion  
Underwater sensor networks encounter unique challenges, such as long propagation delays, limited bandwidth, and high attenuation, which can significantly decrease the performance of communication protocols. Therefore, traditional contention-free methods, specifically time division multiple access (TDMA), are commonly used in underwater sensor networks due to their simplicity and robustness in such demanding environments. Furthermore, clustering methods often rely on TDMA due to their efficiency in scheduling intra-cluster communication, minimizing collisions and energy consumption, which is the primary concern for the power-constrained nodes in underwater conditions. On the other hand, the dynamic nature of underwater environments causes significant variability in energy harvesting conditions. In this study, our primary objective is to augment the accumulated energy of underwater sensor nodes by dynamically selecting TDMA time slots. We specifically emphasized the integration of harvesting awareness into the transmission slot selection strategy. Our investigation revolves around assessing the impact of harvesting-adaptive slot scheduling in response to the varying levels of available energy underwater. Our results show that both TDMA-EH and TDMA-RL improve the network's initial robustness and longevity compared to the baseline TDMA method. The inclusion of energy harvesting in TDMA-EH extends the network's operational period, reflected in higher minimum and mean values for first node dead (FND). TDMA-RL further enhances performance by dynamically adapting time slots based on available energy, thus better sustaining network operations over time. However, TDMA-RL includes the added complexity of reinforcement learning, we would expect it to outperform the simpler TDMA-EH consistently. This similarity might indicate that the learning mechanism is not significant in the earliest phases of node operation compared to what is achieved by simple energy harvesting alone. One future investigation is a guided exploration strategy to quickly identify good actions.  
In terms of half node dead (HND), TDMA-EH and TDMA-RL significantly outperform the baseline, indicating that they can maintain a larger portion of the network for longer periods. TDMA-RL, in particular, shows a slightly higher mean HND, demonstrating the advantages of adaptive scheduling. On the other hand, TDMA-RL shows a higher standard deviation in both HND (24.324) and LND (198.45) compared to TDMA-EH (21.968 and 168, respectively). This suggests greater variability in network performance, which might be due to the adaptive nature of the method leading to inconsistent outcomes across different simulation runs. While adaptive methods can optimize performance, it also shows that they can introduce variability. Q-learning, especially in the initial stages where the algorithm is still learning and exploring the environment. The last node dead (LND) results further  
Figure 9: Number of alive nodes over simulation rounds.  
confirm the superiority of these advanced methods, with TDMA-RL achieving the highest median and mean LND values, indicating its effectiveness in prolonging the overall network lifetime. The TDMA-Best method serves as an ideal benchmark, setting the highest performance targets and illustrating the maximum potential of the network with optimal time-slot assignments. This benchmark underscores the effectiveness of the TDMA-RL method, showing that adaptive learning strategies can approach optimal performance levels.  
Overall, the study highlights the significant benefits of incorporating energy harvesting and adaptive scheduling into UASNs. TDMA-RL stands out for its ability to dynamically manage energy and time-slot allocation, leading to substantial improvements in network longevity and efficiency. The results suggest promising directions for future research, focusing on refining these methods and integrating advanced communication technologies to further enhance network performance and reliability.