# Research on Multi-Strategy Routing Protocol in Flying Ad Hoc Networks
## 5 Experimental Results and Discussion  
In this experiment, OMNeT++ software was used to build the scene. UAV nodes were randomly distributed, and different random topologies were generated by setting different random seeds. The basic parameter settings are shown in Table 1.  
In the existing research on the FANET, the reactive routing protocol AODV, based on the network topology and the greedy geographic forwarding routing protocol (greedy) based on the whole network flood model location service, is widely used. However, the service performance of the above protocols is limited in the three-dimensional high-dynamic network topology environment. The existing common improvements include the combination of intelligent optimization algorithms to improve the routing protocol, such as the AntHocNet [13] routing protocol, and some combined routing protocols, such as the combination of the optimization and greedy PSO-GLFR [21] routing protocol. AntHocNet and GLFR represent the two improved algorithms in the comparison test diagram. This experiment verified the advantages of the proposed RGF routing protocol compared with the traditional routing protocol and the improved routing protocol in the above environment through the simulation of the flight ad hoc network scene by the above parameters. The experiment used three commonly used indicators to analyze the protocol's performance, including the delivery rate, the average delay, and the routing cost, and verified the superiority of each indicator proposed in this paper under different mobile speeds and different nodes. Figures 14-16 show the comparison experiments of each index of the five protocols under different node-mobility speeds. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Simulation time & Total 1000 s (10 topologies, each run for 100 s) \\ Scene size & 10,000 m \(\times\) 10,000 m \(\times\) 6000 m \\ Packet size & 1024 Bytes \\ Packet generation rate & 8-16/s \\ Node number & 35, 40, 45, 50, 55, and 60 \\ Communication radius & 2000 m \\ Transmission speed & 2 Mbps \\ Mobility model & RWP \\ Moving speed & 50–100 m/s, 100–150 m/s, 120–150 m/s, 150–200 \\  & m/s, 200–250 m/s, 250–300 m/s, and 300–350 m/s \\ HELLO message interval of RGF & \(T_{MIN}=0.5\), \(\alpha=1.0035\), \(\beta=0.8\) \\ Greedy forwarding parameters of RGF & \(\rho_{1}=0.5\), \(\rho_{2}=0.1\), \(\rho_{3}=0.3\), \(\rho_{4}=0.1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Figure 14: The effect of velocity on the delivery rate. From Figure 14, it can be seen that the delivery rate of all protocols was not high, mainly due to the small number of nodes and the sparsity of the network. As the node movement speed increased, the delivery rate of AODV underwent a significant decrease, which was due to the lack of location awareness in AODV. The faster the nodes move, the more serious the packet loss is. In contrast, greedy, RGF, GLFR, and AntHocNet encapsulated node movement information in HELLO messages so that the current position of the next-hop node could be calculated before transmitting packets. The low delivery rate of greedy was due to the fact that greedy geographic forwarding is more likely to encounter local minima in a 3D scenario when the node can only wait for the routing hole to eliminate itself before continuing to transmit packets. In contrast, RGF can use the projection face forwarding strategy to continue transmitting packets when greedy forwarding encounters a local minimum, and the minimum local node will also start the reactive path construction at the same time and later transmit packets to jump out of the routing hole through the reactive mechanism forwarding strategy. From the figure, we can see that the delivery rate was the lowest when the nodes moved at 150-200 m/s, which was because the packet delivery efficiency was high when the nodes moved at a slower speed.