# Multi-Objective Optimized GPSR Intelligent Routing Protocol for UAV Clusters
## 5 Experiments and Analysis of Results
### Experimental Parameters and Results
DDQN-MTGPSR is less susceptible to speed changes than the first two. The delivery rate and delay combination prove that DDQN-MTGPSR is more suitable for high-dynamic scenarios than OLSR and AODV. In addition, the ratio of residual energy between OLSR and AODV is lower than that of DDQN-MTGPSR in high-speed scenarios. From the perspective of residual energy variance, DDQN-MTGPSR can still balance the energy consumption of each node as much as possible in high-speed scenarios, which is better than AODV but slightly worse than OLSR. However, from the perspective of comprehensive communication quality performance, it can still be concluded that DDQN-MTGPSR is more suitable for highly dynamic UAV cluster networks. (2) Control variable: scale of network. To test the effect of different scales of the network on routing performance in the same area, the total number of initial nodes was set to 20 and gradually increased to 160 at intervals of 20 nodes, with an average movement speed of 20 m/s for each node. As Figure 7 shows, as the number of nodes in the network increases, the packet delivery rates of both GPSR and DDQN-MTGPSR show an increasing trend. OLSR, on the other hand, tends to exhibit fluctuations within a certain range, while AODV follows an overall decreasing trend. GPSR is more sensitive to density, meaning the greater the number of nodes in the network, the more options there are for GPSR to select as neighboring nodes, thus leading to a higher packet delivery rate. As the number of nodes grows, the network topology becomes more complex, which presents a challenge for timely updates of routing information in OLSR. This can result in some nodes experiencing delays, thus causing fluctuations in the packet delivery rate. With an increase in the number of nodes, the process of establishing routes in AODV becomes more intricate, leading to increased data packet dropouts. For DDQN-GPSR, when the number of nodes is relatively small, its packet delivery rate is indeed lower compared to OLSR and AODV. However, it surpasses GPSR, mainly because of the incorporation of additional state features. Even though DDQN-GPSR still operates as a local topology-based routing protocol, it exhibits significantly reduced packet loss. As the network contains more nodes, the packet delivery rate of DDQN-GPSR tends to remain at a relatively high level, and in certain scenarios, it can match or even surpass OLSR's delivery rate while still outperforming AODV. Compared to GPSR, the PDR of DDQN-MTGPSR is found to be 26.26% higher. Figure 6: Variation of PRE with average speed. AS Figure 8 shows, as the number of nodes increases, the average E2E delay of GPSR decreases first and then increases. This is because increasing the number can increase the number of optional neighbors, which helps to reduce the delay. However, if the nodes become more dense, routing loops and routing holes will cause the delay to increase. The delay of AODV and OLSR increases significantly. AODV establishes routes on demand. The increase in network density leads to longer route establishment time. The rapid increase in the number of OLSR-controlled messages will cause network congestion and increase the delay. The average E2E delay of DDQN-MTGPSR is less affected by the change in the scale of the network and can fluctuate in a relatively low range. Due to the introduction of other routing performance indicators, the algorithm is prompted to select paths with less congestion, more reliable links, and fewer routing holes, reducing the end-to-end delay to a certain extent. Compared to GPSR, the end-to-end delay of DDQN-MTGPSR is reduced by 36.31% on average. As Figure 9 shows, AODV's \(Var_{E}\) exhibits a significant elevation as the number of nodes increases and fluctuates within a relatively high range.