# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 5 Experiments and Results
### Case Study 2: Experiments with AIDA, Using Erceg-SUI Propagation Model
Region B has the highest smart meters concentration per km\({}^{2}\) (316.9 SMs/km\({}^{2}\)), where most of them are very close to each other. Tests with the SUI-model path loss resulted in more unconnected smart meters between each AIDA iteration, generating the need to evaluate more connection possibilities and demanding more processing time (Figure 9). Using the AIDA method with its original propagation model (i.e., taking into account the terrain profile), it was possible to observe a smaller number of candidate positions selected for the installation of routers, suggesting that the topology computed by the AIDA method with the original path loss/diffraction model can plan the smart meter's coverage with a lower number of routers (i.e., selected candidate positions, as shown by Figure 10) and (in most cases) higher average LRP value, resulting in a more efficient solution. By evaluating the number of CPs demanded by AIDA (Table 8, column AIDA (TD approach)), we can notice that the method presented in this study can reduce the number of selected CPs for all the regions when compared with the values calculated by the approaches using Erceg-SUI path loss model. Equation (26) defines the average relative gain between AIDA (executed using its link-specific propagation model) compared to the use of AIDA executed using the Erceg-SUI propagation model. The gain is calculated regarding the number of CPs selected by AIDA executed using each different propagation \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model Parameter** & **Category A Terrain** & **Category B Terrain** & **Category C Terrain** \\ \hline \(a\) & 4.6 & 4.0 & 3.6 \\ \hline \(b\) (in m\({}^{-1}\)) & 0.0075 & 0.0065 & 0.0050 \\ \hline \(c\) (in m) & 12.6 & 17.1 & 20.0 \\ \hline \(\sigma_{\gamma}\) & 0.57 & 0.75 & 0.59 \\ \hline \(\mu_{\sigma}\) & 10.6 & 9.6 & 8.2 \\ \hline \(\sigma_{\sigma}\) & 2.3 & 3.0 & 1.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Erceg-SUI model parameters. model. It aims to show the reduction in the number of candidate positions demanded to connect the smart meters of the region when the original AIDA method is used. \[G_{AIDA}(\%)=\left[1-\left(\frac{1}{3}\sum_{t=A}^{C}\frac{CPs^{AIDA}}{CPs^{ \text{SUI-}}_{t}}\right)\right]\cdot 100 \tag{26}\]  
Using AIDA, the average relative gain regarding the number of selected CPs varied from 40.562% for Region A to 43.273% for Region C.  
Figure 8: Average LRP values (in dBm) obtained by the AIDA method using its original path loss model (AIDA/proposed PL model) compared to the values obtained by the AIDA method using Ereg-SUI path loss model for the terrain Category A (AIDA/SUI-A), Category B (AIDA/SUI-B), and Category C (AIDA/SUI-C). The chart compares the performance of AIDA for the Regions A, B, C, and D, used in the experiments. Figure 9: Processing time (in hours) consumed for the execution of the AIDA method using its original path loss model (AIDA/proposed PL model) compared to the values demanded by the AIDA method using the Ereg-SUI path loss model for the terrain Category A (AIDA/SUI-A), Category B (AIDA/SUI-B), and Category C (AIDA/SUI-C). The chart compares the performance of AIDA for the Regions A, B, C, and D, used in the experiments. In addition, it can be noticed (Figure 11) that the average number of smart meters per selected CP is higher for the AIDA method. This suggests that AIDA can better utilize the connection capacity of a candidate position as it demands a lower final amount of installed routers/gateways compared to using the method with a general path loss model.