## 9 Future Work  
As part of our future work, the overhead of efficient information sharing and decentralized coordination mechanisms will be investigated while maintaining effective coordination among sensor nodes. A comparison of our harvesting-aware method in terms of throughput and energy utilization, thereby providing a comprehensive evaluation of our proposed approach, is planned for future work. Moreover, the efficiency and performance of our transmission slot selection strategy can be improved with advanced multi-agent reinforcement learning algorithms, such as proximal policy optimization (PPO) and deep Q-networks (DQN), and their complexity, will be investigated with a varied size of the network. Additionally, we intend to incorporate variations in channel state to offer a more realistic approach that accounts for the influence of energy harvesting. On the other hand, we plan to validate our proposed method in this study regarding accuracy and reliability to model real-world underwater sensor network scenarios. We plan to compare our simulation results with experimental data obtained from field deployments or test-bed environments, particularly under various environmental conditions and operational scenarios.  
Underwater communication encounters significant delays, higher bit error rates, and limited bandwidth. As a result, it requires adaptive and robust reinforcement learning strategies for time slot allocation. In contrast, terrestrial wireless sensor networks benefit from higher speeds, lower BER, and greater bandwidth, enabling more aggressive and efficient slot allocation through learning algorithms. In another area of future research, we will conduct a comprehensive analysis of delay performance in the adaptive time slot allocation approach, particularly in dynamic underwater environments. This will involve detailed modeling and simulation of delays associated with different slot allocation strategies, as well as empirical validation through field experiments. Our goal is to understand the trade-offs between delay, energy harvesting efficiency, and network throughput to enhance the overall performance of underwater acoustic sensor networks.  
Moreover, the rapid development of new communication techniques such as rate-splitting multiple access (RSMA) and non-orthogonal multiple access (NOMA) presents opportunities. Furthermore, hybrid approaches, combining the strengths of multiple methodologies, such as integrating TDMA with non-orthogonal multiple access (NOMA) techniques, are also promising. To date, these techniques have not been extensively investigated in underwater communication systems. As a future research direction, their potential applicability in underwater environments may be explored. By pursuing these research directions, we aim to contribute to the advancement of UASNs, enhancing their performance and efficiency through innovative communication techniques and more accurate modeling of environmental factors.  
Conceptualization, C.E. and O.M.G.; methodology, C.E. and O.M.G.; software, C.E.; validation, C.E., O.M.G. and P.S.B.; formal analysis, C.E. and O.M.G.; investigation, C.E. and O.M.G.; resources, C.E; data curation, C.E.; writing--original draft preparation, C.E. and O.M.G.; writing--review and editing, C.E. and O.M.G.; visualization, C.E. and O.M.G.; supervision, O.M.G. and P.S.B.; project administration, O.M.G. and P.S.B.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. The APC was funded by authors.  
Not applicable.  
Data are contained within the article.  
The authors declare no conflicts of interest.