## 4 Performance Evaluation
### Convergence Metrics and Sensitivity Analysis  
To evaluate the stability and efficiency of the proposed DAWPC-MAC protocol, we analyze the convergence behavior of Q-values and perform sensitivity analysis on key learning parameters. The achieved results (Table 3) ensure an optimal balance between computational efficiency and performance enhancement. The proposed DAWPC-MAC framework successfully converges within a computationally efficient threshold. The key convergence metrics achieved are:  
* **Number of Iterations for Convergence:** The Q-values stabilize within 5000 to 7000 iterations, significantly reducing computational overhead compared to conventional approaches that often require 10,000+ iterations. * **Convergence Threshold (\(\Delta Q\)):** The convergence is considered achieved when the absolute change in Q-values satisfies \(\Delta Q=|Q_{t}-Q_{l-1}|\leq 10^{-3}\). * **Adaptive Q-value Updates:** The Q-value updates are performed every 10 to 20 actions, minimizing redundant computations while maintaining learning efficiency. * **Batch Size for Training:** A batch size of 16 to 32 samples is used, optimizing memory usage and computational load. * **Neural Network Complexity:** The Q-network is designed with one hidden layer containing 32-64 neurons, ensuring lightweight processing while achieving effective policy learning. These optimizations ensure 30-50% fewer computations, making the training process computationally less intensive without compromising convergence quality. A detailed sensitivity analysis is performed on key RL hyper parameters to ensure robust adaptation to varying network conditions. The results confirm that the following parameter values yield optimal trade-offs:  
By leveraging these optimized parameters, the DAWPC-MAC protocol achieves:  
* **18% improvement** in energy efficiency over baseline MAC protocols. * **12% higher** packet delivery ratio (PDR) compared to conventional MAC schemes. * **25% reduction** in packet collisions compared to static time-slot scheduling methods. The results validate the efficiency of the proposed protocol in underwater wireless sensor networks with superior convergence and adaptability properties along with reduced computational complexity. Figure 8: Energy Efficiency vs. Time Frames. Figure 9 shows how the collision rate in a network changes as the traffic load increases. When the traffic load is low (0-100 frames), the collision rate is quite high, meaning nodes are frequently clashing while trying to access the communication medium. As traffic load increases (100-400 frames), the collision rate decreases significantly, suggesting that the system improves access management and avoiding collisions. After approximately 400 frames, the collision rate stabilizes at a low and steady value (around 0.05-0.1), indicating that the system can manage heavy traffic smoothly. This all shows how DAWPC-MAC or scheduling method is built to be adaptive and collision-free, ensuring the network can keep running fine, even at heavy load. Figure 10 shows how well the network delivers packets as the traffic load increases. When traffic load is low (0-100 frames), the packet delivery ratio (PDR) starts low, around 40%, likely due to inefficiencies or competition for network access. As the load increases (100-400 frames), the PDR steadily improves, reaching about 80%, meaning that the network becomes much better at delivering packets. After 400 frames, the PDR levels around 85-90%, which shows that the system is working at its best and handling heavy traffic efficiently. This pattern highlights how the DAWPC-MAC protocol or system adapts to growing traffic and ensures reliable data delivery even under higher loads. Figure 10: Packet Delivery Ratio (PDR) vs. Time Frames.