## 5 Simulation Results
### Network Performance Comparison  
Figure 3a depicts the message delivery ratio versus the number of vehicles. It is observed that the proposed LQFC protocol outperforms other protocols in terms of the message delivery ratio, especially in comparison with the LQFC-G. This is because our designed LQFC protocol can take full advantage of the flexible deployment of UAVs to select the RN. It is worth noting that the delivery ratio of the LQFC protocol increases can achieve 96% when the number of vehicle nodes increases to 150. In this case, the LQFC-G protocol is only 60%. In addition, we find that the delivery ratio of all protocols increases with an increasing number of vehicles, because with the more the vehicles, the more chances for messages to be transferred. Unfortunately, relay nodes cannot be adaptively selected in the proposals of First Contact, Direct Delivery, Epidemic, Spray and Wait, Prophet and MaxProp, hence their delivery ratio increases slower than LQFC protocol. Hence, Figure 3 shows \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Parameter** & **Value** \\ \hline \multirow{8}{*}{Node} & Transmission mode & Wi-Fi \\  & Mobility model & SPMBM \\  & Vehicle transmission range & 200 m \\  & UAV transmission range & 1000 m \\  & Vehicle speed & 0–50 km/h \\  & UAV speed & 0–70 km/h \\  & UAV flight height & 0–200 m \\  & Number of UAVs & 20 \\ \hline \multirow{8}{*}{Message} & Hello message interval & 0.1 s \\  & Message interval & 25 s–35 s \\ \cline{1-1}  & Message size & 1 MB–5 MB \\ \cline{1-1}  & Buffer & 30 MB \\ \cline{1-1}  & Time to Live (TTL) & 5 h \\ \hline \multirow{4}{*}{Scenario} & Longitude & [33.42\({}^{\circ}\) N\(-\)34.45\({}^{\circ}\) N] \\  & Latitude & [107.40\({}^{\circ}\) E\(-\)109.49\({}^{\circ}\) E] \\ \cline{1-1}  & Simulation area size & 45 km \(\times\) 45 km \\ \cline{1-1}  & Simulation times & 1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Figure 3b plots the average end-to-end delay versus the number of vehicles. From the simulation results, we can find that the LQFC-G protocol has the highest average end-to-end delay at the early stage and then it decreases as the number of vehicles increased. Therefore, when the number of network nodes is small, the network delay with the LQFC-G protocol will increase because no appropriate relay nodes can be selected; however, in the other three protocols, UAVs can improve VANETs connectivity and cooperate with VANETs to bypass obstacles and participate reliably in data transmission to reduce the average end-to-end delay. Therefore, the message can be successfully forwarded even if the number of network nodes is small. Figure 3c illustrates the effect of the number of vehicles on the overhead. The simulation results indicate that although the message delivery ratio and average end-to-end delay of the MTAP protocol are close to those of our design protocol, the overhead of the MTAP protocol rapidly increases with the number of vehicles, because the MTAP protocol uses the multi-copy-based flooding technique for message delivery. Moreover, as expected, the overhead of Epidemic and Spray and Wait rapidly increases with the number of vehicles, because the schemes are based on flooding. In Spray and Wait, each message has a fixed number of copies, and hence the overhead is lower than that of Epidemic. Compared with other three protocols, the Direct Delivery has the lowest overhead ratio because it cannot select a relay node. Furthermore, in comparison with the MTAP protocol, our proposed LQFC protocol can effectively limit the overhead through deleting the redundant copies based on the designed utility function. Figure 3d presents the hop count versus the number of vehicles. From this figure, we can observe that the hop count of the LQFC-G protocol far more than the other protocols. This is because in VANETs, all network nodes are vehicles, which have limited communication range and therefore require multiple hops for relay transmission.