# Performance Evaluation of UAV-Enabled LoRa Networks for Disaster Management Applications
## 4 Performance Evaluation  
The UAV-enabled LoRa system performance is evaluated through a series of simulations performed with the ns-3 network simulator. ns-3 has been chosen over other network simulators for its modular architecture, its speed, its active community and, more importantly, for the support of all the protocols that are necessary for UAV-enabled LoRa, including a WiFi module, MANET modules and external modules for simulating LoRaWAN networks. Additional modules, utility classes, and applications have been developed specifically for implementing the missing features, such as the VSF based mobility algorithm. The developed simulation consists of two separate channels: an AtA channel for the communications between UAVs over WiFi and an AtG channel for the communications between GNs and UAVs through LoRaWAN. Both channels are configured to use the constant speed propagation delay model so that signals propagate with a constant speed equal to the speed of light. This is a reasonable assumption, considering that signals propagate in a low-density air medium. A different path loss has instead been chosen for each channel, to better characterize two extremely different propagation conditions: AtA links are modeled with a Friis propagation model and AtG links with a Log Distance propagation model. The WiFi ad hoc network operates using IEEE 802.11 g, a protocol that is broadly supported and implemented in cheap hardware. The modulation is ERP-OFDM with a maximum throughput of 12 Mbps, which is high enough to support the traffic generated by LoRaWAN GNs. Moreover, the selected modulation is a good compromise in terms of signal range and throughput. The transmission power and receiver sensitivity are set to the default ns-3 values, which correspond to common WiFi settings. The MANET routing protocol is OLSR. Proactively building the routing tables is, in fact, essential for the correct functioning of the UAV-enabled LoRa network mobility algorithm, since the information they contain is used to compute various important parameters. The impact of the routing protocol on the performance of the system has not been investigated as part of this work, but it is reasonable to expect that, at least in highly mobile networks, any protocol that provides fast convergence of routes and frequent periodic table updates would benefit the UAV-enabled LoRa system. The simulation parameters and their values are provided in Table 2. Note that the altitude of UAVs is set to 60 m above the ground, which corresponds to the maximum height of trees in temperate climates. This height is suitable for disaster-ridden areas where the taller infrastructures rarely exist. The terrain is considered flat and the GNs moves at a normal walking speed of 5 km/h which is commonly used in the literature [40]. Figure 5 shows the Average End-to-End Packet Reception Rate (AE-PRR) as a function of different values of \(K_{p}\). It is worth mentioning that the metric AE-PRR is the average percentage of unique packets sent by all GN and correctly received by the BS through the network. To evaluate the performance of the proposed solution, we compare our approach with that of [24]. In the figure, our proposed approach is shown as a solid line, whereas, the approach of [24] is illustrated as a dashed line. In general, it can be seen that the proposed approach outperforms the conventional approach of [24] in terms of improved AE-PRR. In addition to this, it can be noticed that the number of teams influences the range of \(K_{p}\) values that gives the best results in terms of AE-PRR. As an example, with only 4 teams and 12 UAVs, the best results are obtained with \(K_{p}\) in the range [20; 30], while with 5 teams and 12 UAVs the best range becomes [30; 40]. In general, we can observe that a bigger number of GNs requires bigger \(K_{p}\) values. Another immediate and obvious consequence of adding more GNs is the decrease of the maximum achievable AE-PRR, which passes from around 0.9 with one team to almost 0.65 with five teams.