# Multi-Objective Optimized GPSR Intelligent Routing Protocol for UAV Clusters
## 5 Experiments and Analysis of Results
### Experimental Parameters and Results
When the number of nodes is very small, the GPSR protocol exhibits a very high \(Var_{E}\), and as the number of nodes increases, the number of neighbors available for GPSR to choose from increases, and the  
Figure 8: Variation of average E2E delay with the scale of the network. Figure 7: Variation of PDR with the scale of the network. energy consumption becomes more uniform. The \(Var_{E}\) of OLSR is maintained at a relatively low level but shows an overall increasing trend. DDQN-MTGPSR introduces the residual energy of the neighboring nodes, and DDQN-MTGPSR is able to prioritize forwarding nodes with higher residual energy, which plays a role in balancing the energy consumption among nodes, so \(Var_{E}\) is lower under large-scale networks. Nodes' average residual energy variance is reduced by 69.3% on average in DDQN-MTGPSR compared to GPSR. As shown in Figure 10, the more nodes in the network, the more control messages need to be transmitted and processed, so the PRE of OLSR continues to decline. AODV shows a downward trend as a whole. The more nodes, the more complicated the route establishment process of AODV and the more energy consumed. The GPSR protocol is more sensitive to the density of the node. It can be used with fewer neighbors with extremely low density. It may trigger extremely frequent peripheral repost mode, so the routing efficiency is low. As the density of the node increases, this situation has improved, and the surplus energy proportion of GPSR networks is higher than that of AODV and OLSR. Because the DDQN-MTGPSR has a longer radio signal field, compared to GPSR's control overhead, but due to the introduction of other routing performance indicators, the routing efficiency of DDQN-MTGPSR is higher, and its overall expenses will not be significantly higher than that GPSR has the phenomenon of ups and downs fluctuations between the two. Compared with GPSR, the surplus energy of DDQN-MTGPSR has increased by 8.709% on average. Compared with GPSR, with the increase of network scale, DDQN-MTGPSR basically has a comprehensive improvement on the evaluation indicators. Based on all evaluation indicators to analyze, OLSR and AODV are not suitable for large-scale scenarios. The extension of the former's end-to-end is significantly increased with the increase in the number of nodes. Regarding the trend of decline and increase, unlike OLSR and AODV, the increase in network scale will not have a significant negative effect on the performance of DDQN-MTGPSR in terms of delay and delivery rate. In large-scale scenarios, its delivery rate is higher than AODV and close to OLSR. The delay can be lower than AODV and lower than OLSR under certain high-density conditions. In addition, the surplus energy of OLSR and AODV will gradually be lower than the DDQN-MTGPSR with the increase in nodes. From the perspective of the remaining energy difference, the DDQN-MTGPSR is better than AODV due to the increase in the number of nodes and can be close to OLSR, indicating that DDQN-MTGPSR can balance the energy consumption of each node in large-scale networks. In summary, DDQN-MTGPSR is suitable for large-scale UAV cluster networks. Figure 9: Variation of \(Var_{E}\) with the scale of the network. Combining all the experimental results under the two experimental conditions and considering the comprehensive performance of the network, including communication quality of service and energy consumption, DDQN-MTGPSR is more suitable for large-scale and highly dynamic UAV clusters than the other three routing protocols.