# PDDQN-HHVBF Routing Protocol Based on Empirical Priority DDQN to Improve HHVBF
## 5 Performance Analysis and Simulation
### Comparative Analysis of Simulation Results
Because it considers the historical Q value by energy, PDR and end-to-end delay, it performs better than the  
Figure 12: The PDR of VBF, HHVBF, DQELR, ROEVA, and PDDQN-HHVBF protocol in different speed of nodes. VBF and the HHVBF. However, it just consider the static node, not the mobility of nodes. Thus, it performs worse than the DQELR and the PDDQN-HHVBF in this circumstance. The DQELR protocol consumes less energy because of its uni-cast strategy of finding the optimal relay node. However, in this experimental environment, there will still be re-transmission packets and data collisions caused by packets loss. Thus, the energy tax is larger than the PDDQN-HHVBF protocol proposed in this paper. The PDDQN-HHVBF protocol proposed in this paper is the best performing protocol among the five protocols. It not only has a lower energy tax, but also has a stable trend and the best robustness. This is because the PDDQN-HHVBF protocol has the highest PDR in this network environment, the least packets loss, and the least number of data re-transmissions and collisions, so it consumes less energy. Then, we explore how the mobility of nodes will affect the end-to-end delay of the five algorithms. In the Figure 14, as the speed of node movement increases, the end-to-end delay of the five algorithms also increases. This is because the number of data packets re-transmissions increases due to the node movement, which makes the delay increase with the node movement speed.In this experimental environment, the end-to-end delay of VBF protocol and HHVBF protocol is significantly higher than that of other three protocols. It is because that both VBF and HHVBF protocols will repeat receiving of data packets and data packet conflicts, thus increasing the number of data re-transmissions and delay, especially when the node movement speed increases. The protocol proposed in this paper needs to query the Q value of candidate relay nodes, which leads to spending extra time in Q value request and reply, so the delay is higher than the DQELR and the ROEVA. This because the Q values of the DQELR protocol and the ROEVA protocol consider the link delay, these two protocols perform best in the delay indicators. When the node moves at a low speed, the ROEVA performs better than DQELR. However, when the node moves at a faster speed, the ROEVA does not consider the node mobility, resulting in a higher delay than DQELR. Overall, the DQELR performs best in end-to-end delay influenced by high mobility of nodes. Then, in this environment, we compare the energy consumption of the AUV \"Store-Carry-Forward\" method and the AUV \"Forward-Confirm\" method of sending data packets. When using the \"Store-Carry-Forward\" method, the energy consumed by each data packet  
Figure 13: The energy tax of VBF, HHVBF, DQELR, ROEVA, and PDDQN-HHVBF protocol in different speed of nodes. of the AUV is: 10 J for data transmission, 0.5 J for requesting Q-value control packets, and 0.05 J for receiving and returning Q-value control packets. If there is no suitable relay node, it will not forward the data and continue to detect new data until the AUV finds a suitable relay node to forward. When the AUV uses the \"Forward-Confirm\" mechanism to send a data packet, the AUV will wait for the node to receive a successful return confirm packet. As shown in Figure 14, the energy consumption of the AUV using the \"Store-Carry-Forward\" mechanism to send 1000 data packets fluctuates around 10,500 J, while the AUV using the \"Forward-Confirm\" mechanism accelerates the random movement speed of the node. As a result, the AUV re-transmits data packets multiple times and wastes a lot of energy. Figure 14: The end-to-end delay of VBF, HHVBF, DQELR, ROEVA, and PDDQN-HHVBF protocol in different speed of nodes. Figure 15: The AUV energy consumed of “Store-Carry-Forward” and “Forward-Confirm” in different speed of nodes. In the following experiments, we explore how the number of nodes in M-UWSNs affects the PDR, energy tax, end-to-end delay and Lifetime metrics of the five algorithms.