## 6 Conclusions and Future Directions  
This paper presents a novel receiver-initiated DAWPC-MAC technique that combines adaptive power control, dynamic wake-up scheduling, and Deep Q-Learning DQN to improve energy efficiency and improve collision avoidance and synchronization in UWSNs. By incorporating a priority-based communication system and intelligent learning mechanisms, the protocol ensures reliable and timely delivery of critical data while minimizing energy consumption. In the case of the receiver-initiated MAC protocol, the land-based BS transmits communication signals to buoys using satellite communication. This satellite connection is for communication to the UWSN over long ranges. The BS transmits commands, data requests, or synchronization signals as required. When the signal arrives at the buoy, it is transmitted vertically to the sink node directly above it, which acts as the main communications hub. DAWPC- MAC protocol improves energy efficiency by regulating the wake-up and sleep cycles of nodes. A receiver-initiated wake-up mechanism is used, in which a low-power wake-up signal (WS) is broadcasted by the courier node as the receiver node, notifying neighboring transmitter nodes (Tx) about the upcoming communication window. Only Tx nodes with data to send respond, minimizing energy consumption. Resource allocation and scheduling are dynamically optimized by the deep Q-learning algorithm (DQN) based on real-time conditions such as considered water environment salinity, temperature along with node positions, traffic load, and energy levels. Proposed DAWPC-MAC ensures energy-efficient and reliable time-sensitive data transmission with packet delivery ratio (PDR) improves 14% compared to existing schemes TDTSPC-MAC, DC-MAC and ALOHA MAC and in-terms of throughput more than 70% improvement along with utility more than 60% improvement that contribute in network longevity and operational efficiency for time critical underwater applications compared to traditional methods. Furthermore, the use of experience replay and target networks by DQN has led to more consistent learning and faster convergence, affirming its effectiveness in dynamic underwater settings.In the future we will work on the DQN based MAC protocol for the Internet of underwater things (IOUT) by incorporating more underwater environmental parameters data for the physical characterization of the dynamic channel and considering hybrid mode of communication between sensors, carrier node, base station and AUV/ROV like acoustic, optical and electromagnetic from small to large networks.  
Conceptualization, Q.G. and W.U.R.; methodology, Q.G. and W.U.R.; software, W.U.R. and F.Z.; validation, Q.G., W.U.R. and F.Z.; formal analysis, F.Z. and W.A.; investigation, W.U.R., Q.G. and F.Z.; resources, Q.G. and F.Z.; data curation, M.T., M.I.K. and W.A.; writing--original W.U.R.; writing--review and editing, W.U.R., M.I.K. and W.A.; visualization, W.U.R., M.A., M.I.K. and F.Z.; supervision, Q.G. and F.Z.; project administration, Q.G. and F.Z.; funding acquisition, Q.G. and F.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by: 1. Natural Science Foundation of Heilongjiang Province of China: JQ2023A004. 2. Shenzhen Science and Technology Program under Grant No. JSGG20220831103800001. 3. Key Research and Development Program of ShanDong Province under Grant No. 2022 CXGC020409.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.