# LoRaWAN Networking in Mobile Scenarios Using a WiFi Mesh of UAV Gateways
## 5 Simulation Results
### Comparison of LoRaUAV VSF and DF VSF Algorithms  
A comparison between the LoRaUAV VSF algorithm (see Section 3.2) without extensions, and the VSF algorithm described by [PERSON] et al. (2018) (DF algorithm) has been performed. Before starting the analysis, it must be clarified that the \(K_{p}\) parameter coincides with the proportionality factor of \(K_{ALA}\) in the LoRaUAV VSF algorithm and it coincides, instead, with \(K_{ALA}\) itself in the DF VSF algorithm. An AE-PRR comparison between the two algorithms is shown in Figure 4 for a scenario with three and five teams of GNs. For clarity, the confidence interval is not shown. It is possible to notice that the AE-PRR of both algorithms follows approximately the same trend for all configurations, presenting the same regions of increase and decrease and the same range of optimal values. However, the AE-PRR produced by the LoRaUAV VSF algorithm seems slightly better than the one generated by the DF VSF algorithm for most \(K_{p}\) values. To verify this hypothesis, the curves generated by the two algorithms have been compared by computing the point by point euclidean distance between them for each configuration. On average, LoRaUAV produces an AE-PRR increment which lies between \(0.03\%\) and \(4.6\%\) independently of the value of \(K_{p}\) that is used. The developed VSF algorithm produces more benefits when a large number of teams is deployed (e.g., five teams). However, for most \(K_{p}\) values, improvements fall inside the confidence interval of the DF VSF algorithm. It is therefore not possible to assert that the LoRaUAV VSF algorithm performs better in general. In fact, changing just the weights of the forces is not by itself sufficient to achieve a significant AE-PRR increase. Nevertheless, even if small and limited to particular configurations, the improvements introduced by the developed algorithm  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{2}{c}{**LoRaWAN**} & \multicolumn{2}{c}{**WiFi and Routing**} \\ \hline GN Tx Power & 14 dBm & PHY protocol & IEEE 802.11g \\ Class & Class A & Modulation & ERP-OFDM \\ Bandwidth & 125 kHz & Frequency & 2.4 GHz \\ Frequency & 868.1 MHz & Tx Power & 16.02 dBm \\ SF & 7 & Rx Sens. & \(-99\) dBm \\ GW Sens. (dBm) & \(-124\) (SF 7) & Bitrate & 12 Mbps \\ Packet size & 10 bytes & MANET protocol & OLSR \\ Packet period & 30 s & & \\ \hline \multicolumn{2}{c}{**VSF Algorithm**} & \multicolumn{2}{c}{**CRM Algorithm**} \\ \hline \(\Delta T\) & \(10\) s & \(P_{max}\) & 30 \\ \(t_{AIG}\) & 40 s & \(P_{shared}\) & 0.5 \\ \(LB_{\text{req}}\) & 20 dBm & \(t_{load}\) & 3 min \\ \hline \multicolumn{2}{c}{**MP Algorithm**} & \multicolumn{2}{c}{**Firegighter Mobility Model**} \\ \hline \(t_{\text{\emph{lost}}}\) & 3 min & \(s_{i}^{y}\)\(\forall j\) & 300 m \\ \(t_{r}\) & 40 s & \(s_{i}^{y}\)\(\forall j\) & 100 m \\ \(t_{exp}\) & 10 min & \(d_{r}^{i}\) & \(\mathcal{U}(50,250)\) m \\ \(k_{max}\) & 3 & \(t_{raw}\) & 5 min \\ \(s_{thr}\) & 0.6 & BS position & \((S/2,300)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of fixed simulation parameters.  
\begin{table}
\begin{tabular}{l l} \hline \hline N. of UAVs & \(4,8,12\) \\ N. of teams & 1-5 \\ GNs per team & 20 \\ Sim. Time & 2000 s \\ Area side & 2000 m \\ \(K_{p}\) & \([1,50]\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Default simulation parameters.  
come at no significant additional cost, apart from a delay penalty affecting some packets and correlated with the higher PRR. As such, at this stage, basic LoRaUAV becomes at least validated in comparison with DF. Further comparison regarding the frequency and duration of disconnection times is provided in Section 5.4.