# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 5 Case Study: A DTN-assisted FANET Framework
The duplication of a packet increases the load on the network, so to end this, there is a time-to-live (TTL) period for each packet. After this period, the packet would be discarded by its custodian. 3. Steps one and two continue between the UAVs hop-by-hop till the packet reaches the destination node at the application layer. The destination node then broadcasts an ACK indicating the packet has reached the destination. The UAVs that receive the ACK store it in their buffer and exchange it with other UAVs carrying the packet till the packet TTL runs out. As a result, all other custodians carrying the packet are notified of the delivery and discard the packet from their buffer. Figures 10 and 11 show performance comparisons between this LAROD-based model and other DTN-assisted FANET techniques proposed in [22]. Simulation is carried out using the opportunistic network environment (THE ONE) simulator with simulation parameters given in Table 3. Random Waypoint mobility is considered. The rest of the parameters are the same as those in [22] unless otherwise specified. The UAV nodes are supposed to be small, e.g., DJI mini2 20 \(\times\) 20 \(\,\mathrm{cm}^{2}\), and their minimum distance cannot be less than 1 m. We can see that the LAROD-based model slightly outperforms other DTN-assisted FANET models in terms of delivery ratio. We note that although the MaxProp model slightly outperforms the LAROD-based model as shown in Figure 10, MaxProp's latency is much higher than the LAROD-based approach as shown in Figure 11. Figure 12 shows the average buffer time for each delivered message, and we can see that the MxProp routing has the most efficient buffer time since it can prioritize the packet queuing with additional processing at the nodes. On the other hand, the Epidemic routing has the worst performance in terms of average buffer time since it broadcasts packets to all nodes and UAVs keep the packets in their buffer. Figure 13 shows the average number of hops in each protocol. We can see that an increase in the number of UAVs slightly increases the average number of hops. LAROD-based model is being more affected by the number of UAVs compared to the other models. Figure 14 demonstrates the overhead ratio as the difference between relayed and delivered messages on the basis of delivered messages [18]. We note that the Spray and Wait protocol has the lowest overhead, while the Epidemic and PROPHET models have the largest overheads. \begin{table}
\begin{tabular}{c c} \hline \hline
**System Parameters** & **Corresponding Value** \\ \hline Number of UAVs & 2, 6, 10 \\ Speed of UAVs & Varies between 1 m/s to 5 m/s \\ Speed of mobile user & 1 m/s \\ Interface model & Bluetooth (IEEE 802.15.1) and WiFi (IEEE 802.11b/g/n) \\ Transmit speed & 250 kBps \\ Message size & 250 KB \\ Transmit range & Bluetooth: 20 m, WiFi: 100 m \\ Buffer Size & 10 GB \\ Mobility model & Random Waypoint \\ Message TTL & 300 min \\ Simulation running time & 12 h \\ \hline \hline \end{tabular}
\end{table} 
Table 3: System numerical parameters. Figure 11: Latency comparison of LAROD-based model and a few well-known DTN routing protocols. Figure 12: Average buffer time of a delivered message \(\leq\) TTL = 300 min.