## 3 Proposed System Model
### DAWPC-MAC Model Overview
Nodes with sufficient residual energy to sustain close to idle duty cycles (i.e., able take off a higher proportional fraction of energy reserves to their transponders) will allow more frequent transmission (operating at a higher duty cycle), and nodes with sparse energy reserves will reduce activity to conserve energy. This mechanism allows the energy resources to be engaged across the entire network by allowing aggregated overall energy performance. Furthermore, within a time slot, an adaptive time offset process is proposed between slots to reduce the possibility of collisions at the receiver. These techniques reduce collision at the receiver, even when synchronization error or overlapping slots are present. Hence, the probability of simultaneous arrivals of packets at the receiver is greatly diminished, providing a reliable communication. The DAWPC-MAC protocol combines wake-up scheduling and resource allocation to provide real-time optimization according to the network states (i.e., active node location, traffic flow, and energy status). The courier node acts as receiver node (Rx) for the delivery and makes intelligent decisions on wake-up timing, transmission power adjustments, TDMA slot allocation, and traffic prioritization, as it learns over time, and thus can give priority to critical data packets over the non-critical ones, e.g., environmental measurements from routine weather monitoring. So this adaptive mechanism greatly reduces energy consumption with reliable communication and allows the protocol to adapt instantaneously to dynamic changes in both the energy and traffic profile of the network. However, collisions are efficiently avoided by adopting to approach depicted in Figure 3.  
Figure 3 illustrates the critical challenge of space-time uncertainty in Underwater Wireless Sensor Networks (UWSNs) and highlights the impact of different time slot scheduling strategies on network performance. Due to the unique underwater communication environment--characterized by long propagation delays, dynamic topology changes, and limited bandwidth--achieving collision-free and power-efficient medium access control (MAC) is a significant challenge. As shown in Figure 3a, this scenario represents a conventional time-slot scheduling approach where nodes are pre-assigned specific transmission slots. However, due to unpredictable propagation delays and interference, overlapping transmissions result in packet collisions. These collisions lead to increased retransmissions and energy consumption, reducing network efficiency. Figure 3b demonstrates an optimized slot allocation where transmissions are scheduled to prevent collisions. While this method ensures interference-free communication, it lacks adaptability to changing network conditions, making it inefficient in dynamic underwater environments. Figure 3c The proposed **Deep Q-Learning-based adaptive MAC protocol** demonstrates the dynamic  
adjustment of time slots and transmission power levels based on network conditions. The adaptive scheduling mechanism effectively minimizes collisions, optimizes energy usage, and improves network throughput by leveraging reinforcement learning to make intelligent scheduling decisions. The insights from Figure 3 reinforce the necessity of **adaptive MAC protocols** in UWSNs, where static scheduling approaches fail to address the challenges posed by underwater channel dynamics. The proposed **Deep Q-Learning-based approach** ensures robust communication by continuously learning and optimizing slot assignments and power levels in real-time, thereby enhancing overall network efficiency. The wake-up signal power \(P_{\text{WS}}\) is initially set to a low value to conserve energy. As no response is received from nearby nodes, the power is incrementally increased to improve coverage and the likelihood of establishing communication. Once communication is successfully established or no further response is required, the system is transitioned to a sleep mode, where the power is reduced to a minimal level.