# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 5 Experiments and Results
### Case Study 1: Experiments with AIDA, Using Link-Specific Propagation Model
\begin{table}
\begin{tabular}{l|c|c|c|c|c|c|c|c} \hline \hline  & \multicolumn{2}{c|}{**Region A**} & \multicolumn{2}{c|}{**Region B**} & \multicolumn{2}{c|}{**Region C**} & \multicolumn{2}{c}{**Region D**} \\ \hline
**Metric** & **BU** & **TD** & **BU** & **TD** & **BU** & **TD** & **BU** & **TD** \\ \hline No. of iterations & 3 & 3 & 3 & 3 & 2 & 2 & 2 & 2 \\ \hline No. of unconnected SMs & 3 & 3 & 0 & 0 & 0 & 0 & 4 & 4 \\ \hline Percentage of connected SMs & 99.998\% & 99.998\% & 100.0\% & 100.0\% & 100.0\% & 100.0\% & 99.934\% & 99.934\% \\ \hline No. of CPs selected for positioning gateways and routers & 294 & 249 & 58 & 44 & 190 & 169 & 48 & 44 \\ \hline No. of gateways & ** & 140 & ** & 44 & ** & 18 & ** & 7 \\ \hline No. of routers & ** & 109 & ** & 0 & ** & 151 & ** & 37 \\ \hline Average No. of SMs/gateway & ** & 1078.2 & ** & 1276.3 & ** & 1199.1 & ** & 871.7 \\ \hline No. of SMs/CP & 513.4 & 606.2 & 968.2 & 1276.3 & 113.6 & 127.7 & 127.1 & 138.7 \\ \hline Average LRP for the links \(SM\Leftrightarrow SM\) and \(SM\Leftrightarrow CP\) (dBm) & \(-72.15\) & \(-73.43\) & \(-73.72\) & \(-79.14\) & \(-67.6\) & \(-72.86\) & \(-71.93\) & \(-75.57\) \\ \hline \% of high-quality links & 99.883\% & 99.998\% & 99.407\% & 99.361\% \\ \hline \% of medium-quality links & 0.081\% & 0.002\% & 0.459\% & 0.573\% \\ \hline \% of low-quality links & 0.036\% & 0\% & 0.134\% & 0.066\% \\ \hline Max. No. of CPs in SM range * & 15 & 12 & 10 & 5 \\ \hline Average No. of CPs in SM range * & 5.8 & 6.2 & 4.3 & 1.9 \\ \hline Processing time (\(h\)-mins) & 15:47:44 & 03:52:07 & 00:55:04 & 00:14:19 \\ \hline Relative gain (\(G_{r}\)) & 15.3\% & 24.1\% & 11.1\% & 8.3\% \\ \hline \hline \end{tabular}  
* For the 1 st iteration, considering only high-quality links; ** Computed only for the approach that selected less CPs positions.  
\end{table} 
Table 6: Final results comparing bottom–up (BU) and top–down (TD) approaches. Figure 7: AIDA iterations for Region A. 24.1% for Region B, 11.1% for Region C, and 8.3% for Region D), the average reduction in the number of CPs (comparative gain) is 14.7%. Regarding the number of CPs computed by the top-down approach, Region A demanded 249 positions. Regions B and D, in turn, resulted in the same number of candidate positions used (44 positions), despite having very different amounts of SMs (56,157 SMs for Region B and 6106 for Region D). This behavior is justified because Region B has a higher concentration of SMs per km\({}^{2}\). In contrast, region D has the geographically dispersed meters, requiring the positioning of various key devices to ensure communication. For Region C, a total of 169 devices was estimated as it is also a region with high sparsity (7.2 SMs/km\({}^{2}\)). Analyzing the first iteration of the method, the number of candidate positions with which a meter has viable conditions for establishing a connection varied (on average) from 1.9 possibilities for Region D to 6.2 possibilities (on average) for each SM in Region B. For Region A, it was observed that an SM could establish a connection with up to 15 candidate positions.