# Opportunistic Multi-Technology Cooperative Scheme and UAV Relaying for Network Disaster Recovery
## 6 Performance Evaluation
### Cooperative Multi-Tier Data Relaying
#### 6.1.2 Obtained Results  
Figure 8 depicts the number of alive nodes over time. More specifically, 400 initial nodes randomly distributed in an urban area disseminate their data in accordance with the three schemes: baseline, static, and the dynamic one. The baseline scheme, in fact, performs poorly in terms of number of alive nodes over time and energy fairness among them: all the nodes die (i.e., no energy availability left) in the network within a relatively short period of time with regard to the golden relief time, and such a trend is almost linear on time. Such is justified by the fact that each node is accountable only for itself, hence it switches on all the necessary network interfaces. This leads to a big number of nodes having their battery depleted at the same time instant. By contrast, the static scheme outperforms the baseline one, leading to a higher number of alive nodes at a given time instant. In fact, the static scheme leads to at least 50 alive nodes more than the baseline scheme and such a gap increases over time. Furthermore, if offers a smoother dynamic of nodes dying in the network, i.e., fewer nodes die at the same time instant. Such a scheme almost doubles the time period within which there is at least one alive node in the network. Our proposed dynamic scheme of clustering and CH selection outperforms the baseline and the static scheme. As it introduces energy expenditure fairness among nodes, it increases the number of alive nodes at a given time instant compared to the other two schemes. Moreover, most of the nodes die alone, or in smaller groups. In fact, such leads to have nodes with comparable energy levels in the network over time. Therefore, such results in the last alive nodes dying together or dying within a short period of time. Such explains the fact that the static scheme slightly outperforms the dynamic one in the last two hours, approximately from hour 6 to 8.  
Figure 9 depicts the difference between the highest and lowest energy values in the network over time. Similar to Figure 8, the cooperative-based schemes, i.e. static and dynamic, outperform the baseline one, leading to a lower energy difference. That is, the energy levels of the nodes present smaller gaps among each other, i.e., the energy burden put across the nodes is more equally distributed. The initial rise of the difference between the energy levels could be explained by the fact that the nodes with initial low available energy levels deplete their batteries soon after the data dissemination starts.  
Figure 8: Number of alive nodes over time.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Disaster area & 10 km \(\times\) 5 km \\ Drone speed & 10 m/s \\ Minimum hovering time & 5 s \\ Drone-\(n_{3}\) tier node data exchange time & 2 s \\ Bluetooth tx range/power consumption & 100 m/50 mW \\ WiFi tx range/power consumption & 200 m/70 mW \\ Cell tx range/power consumption & 500 m/120 mW \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of used parameters.  
However, the energy expenditure burden is then equally distributed among the remaining nodes in the dynamic case, hence the difference between the highest and lowest energy values is well below the one of the static case. In fact, the static case puts the energy burden on few nodes that are designated as CHs (whose energy depletes fast), therefore the energy gap between the nodes increases. Moreover, the difference between the energy levels in the baseline case presents a steep slope because all the nodes in the network are responsible to communicate directly with a drone, hence they switch on all the network interfaces at the same time. Though the energy burden is equally distributed, the energy of the nodes depletes almost twice as fast as the cooperative-based schemes.