# On the Performance of a UAV-Aided Wireless Network Based on NB-IoT
## 5 Results and Discussion  
In this section, we will analyze our NB-IoT network performance and discuss the achieved results. Simulations are carried out with the parameters listed in Table 4. As previously mentioned, we are jointly studying a number of metrics, being:  
* the access rate, \(R_{\text{acc}}\);
* the percentage of nodes completely served, \(S_{\text{suc}}\);
* the mean latency perceived by nodes, \(\Delta\tau_{\text{avg}}\);
* the network throughput, \(T_{\text{net}}\);
* the mean energy consumption of NB-IoT nodes, \(E\). Figure 4: Example of TSP-driven path (blue line) and clusters with nodes (red dots) location; the dark blue circle indicates the start of the trajectory. Figure 3: Example of Paparazzi path (blue line) and clusters with nodes (red dots) location; the dark blue circle indicates the start of the trajectory. These metrics are computed via the following formulas:  
\[R_{\text{acc}}= \frac{n_{\text{succ}}}{n_{\text{att}}} \tag{8}\] \[S_{\text{suc}}= \frac{n_{\text{suc}}}{n_{\text{tot}}}\cdot 100\] (9) \[\Delta\tau_{\text{avg}}= \frac{\sum_{u=1}^{n_{\text{tot}}}(\tau_{u,\text{tx}}-\tau_{u, \text{start}})}{n_{\text{tot}}}\] (10) \[T_{\text{net}}= \frac{n_{\text{tot}}^{\text{tot}}}{u}\] (11) \[E= \frac{\sum_{u=1}^{n_{\text{tot}}}E_{u}}{n_{\text{tot}}} \tag{12}\]  
We denote as access rate, \(R_{\text{acc}}\) in Equation (8), the fraction of the number of succeeded transmissions, \(n_{\text{suc}}\), over the overall attempted by anyone of the IoT nodes, \(n_{\text{att}}\). In this way, we analyze how frequently a node \(u\) may get access to the channel. When the \(R_{\text{acc}}\) value gets close to zero, the number of access attempts has to increase before success. Equation (9) let us identify how effective is the UAB service. In fact, \(S_{\text{suc}}\) counts the number of nodes successfully transmitting their traffic demand over the total number of present IoT nodes, \(n_{\text{tot}}\), in percentage. Then, Equation (10) computes the latency or delay for the node \(u\) spanning from the time in which the request is started, \(\tau_{u,\text{start}}\), until when the transmission succeeded, \(\tau_{u,\text{tx}}\). If a node is not able to transmit its packet, we do not consider it in the average latency computation. Equation (11) computes the overall network throughput by summing the one of each node \(u\), \(t_{u}\). Finally, Equation (12) averages the energy consumed by all nodes present in the service area, \(n_{\text{tot}}\). To start with, Figure 5 shows the percentage of IoT nodes served by the UAB, that means their traffic demand is completely fulfilled. Its value is represented while varying the UAB speed, \(v\), and for the different trajectories. This picture has some relevant outcomes. First, the performance is not the same when varying speed; for each trajectory, it drops fairly below the 50% of served nodes. This is clearly related to the time interval in which the UAB is able to maintain a robust radio connection with each node. In fact, if the received power falls below the receiver sensitivity before the sign \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Area side, \(L\) & 10 km \\ Mean number of nodes per cluster, \(m\) & 100 \\ Intensity of parent points, \(\lambda_{\text{P}}\) & 5 \\ Locations’ standard deviation, \(\sigma\) & 100 \\ Packet size, \(B\) & 500 bits \\ UL transmit power, \(P_{\text{tx}}\) & 14 dBm \\ Antennas loss, \(A_{\text{L}}\) & 2.5 dBi \\ Penetration loss, \(\eta\) & 40 dB \\ Noise power, \(P_{\text{N}}\) & 30 \(\times\) 10\({}^{-17}\) W \\ \(\alpha\) & 9.6117 \\ \(\beta\) & 0.1581 \\ \hline Channel bandwidth, \(B_{c}\) & 180 KHz \\ Subcarrier spacing & 3.75 kHz \\ Available subcarriers, \(N_{\text{SC}}\) & 48 \\ Carrier frequency, \(f_{c}\) & 800 MHz \\ RU duration & 32 ms \\ MCS index, \(I_{\text{MCS}}\) & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Radio and Network Parameters.