# Flying Real-Time Network to Coordinate Disaster Relief Activities in Urban Areas +
## 4 Network Deployment Model
### Performance Evaluation  
In order to evaluate the performance of the proposed algorithm for the deployment of TWUs, we assumed a terrain with elevations as shown in Figure 6. The orography of this physical scenario was inspired in the area affected by landslides in Rio de Janeiro (Brazil) in 2011 [9]. As part of the first response process, it is required to create a network of nodes that allows the SAR teams to be connected with the IC. These nodes were located in the positions shown in Table 1, referred to as the total deployment area.  
The network nodes had a radial communication range equivalent to 20% of the width of the deployment area. The parameters chosen were \(\alpha=1\) and \(\beta=1\); moreover, optimizations based on genetic algorithms (GAs) and particle swarm Optimization (PSO) were used to iteratively improve the  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Node** & **Coordinates (X,Y)** \\ \hline IC & (0.1, 0.1) \\ SAR 1 & (0.3, 0.9) \\ SAR 2 & (0.75, 0.3) \\ SAR 3 & (0.8, 0.85) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Positions of the first response teams at the initial deployment stage.  
Figure 5: Dynamics of the adaptive network model.  
Figure 6: Terrain elevation map considered in the deployment example.  
nodes' deployment in order to attempt to reach the optimum setting. These optimization methods were implemented in the programming language GNU Octave version 4.0, and the implementations were run in a personal computer with a Intel Core i7 7700 hq @2.80 GHz processor, 16 GB RAM, and a Nvidia GeForce GTX1060 graphics processor.  
Both PSO and GA require the generation of an initial population from which each algorithm evolves by improving the deployment solution, which in any case is sub-optimal. As mentioned before, random initialization can have a high computational cost due to the large number of nodes required to achieve connectivity between the SARs and the IC. To improve the initialization time and reduce the total execution time of the optimization methods, the deployment algorithm proposed in Section 3.2 was used as initial condition; small changes in these nodes positions were then introduced to represent variability in the initial setting.  
When the GA-based optimization was used, the algorithm execution was limited to 3000 s, reaching a total of 173 generations, and the best deployment solution included nine TWUs. On the other hand, the PSO algorithm finished its execution after 2978 s, completing 200 iterations and the best solution considered 11 TWUs. The scores of these best solutions were 16.439 and 18.368 for GA and PSO respectively.  
The plots shown in Figure 7 indicate that the best deployment of TWUs for both GA and PSO was done surrounding the sloped area. This is because the nodes have to be close to each other in order to achieve visibility in mountain or valley zones; this significantly increases the network size.  
Figure 8 shows the historical best fitness value of each algorithm. The left plot displays the population best and mean fitness quality of each generation and the right figure shows the fitness function value of the best particle in the swarm for every iteration. It can be seen that PSO required fewer iterations to achieve the best solution compared to GA, but it tended to stagnate while GA continued improving the quality of the solution, although this improvement was very small.  
Figure 7: Sub-optimal network deployments generated by genetic algorithms (GAs) (**left**) and particle swarm optimization (PSO) (**right**). Green nodes represent the IC, red nodes are SAR teams, and blue nodes are TWUs.