# 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  
In this section, we describe the experiments with the AIDA method using its original propagation model, which calculates losses considering the detailed terrain profile between the communication devices in the analysis. Figure 7 shows the result after each process iteration in region A (150,951 SMs). It can be noticed that, at the end of each iteration (iterations 1, 2, and 3), the number of unconnected SMs (points in black) decreases. The green points show the SMs connected to the CP's positions via one-hop or multihop connections. The figure also presents the candidate positions available and the ones effectively selected to connect the SMs. A summary of the regions A, B, C, and D results is presented in Table 6. For each iteration, AIDA performs the processing with two approaches (bottom-up and top-down) using the same set of SMs, i.e., the list of unconnected SMs resulting from the previous processed iteration with the approach that used the smallest number of CPs. Regarding the main characteristics of the two different approaches, it can be said that: the BU approach prioritizes the connection of a smart meter with the candidate position that presented higher LRP values, resulting in a final network topology that tends to present a higher average LRP value when compared to the other approach; the TD approach, in turn, prioritizes the use of fewer candidate positions than the BU approach, respecting the minimum LRP value to establish the connections between candidate positions and smart meters around them, reaching a final average LRP value slightly smaller than the one obtained by the other approach. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Region** & **No. of SMs** & **BB Area (km2)** & **SMs/km2** & **Poles** & **DAs** \\ \hline A & 150,951 & 4427.2 & 34.1 & 62,412 & 80 \\ \hline B & 56,157 & 177.2 & 316.9 & 15,754 & 19 \\ \hline C & 21,583 & 3001.8 & 7.2 & 26,005 & 39 \\ \hline D & 6106 & 622.6 & 9.8 & 8250 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Information about the regions used in the experiments. Considering this, for the analyzed regions, the top-down approach presented better results overall, especially regarding the number of CPs selected. By evaluating the number of _CPs_ demanded by each clustering approach (Table 6), we notice that the top-down approach can reduce the number of selected key devices for all the regions. Equation (19) defines the relative gain between the top-down (_TD_) and the bottom-up (_BU_) approach regarding the reduction in the number of CPs. \[G_{r}(\%)=\left(1-\frac{C_{s}^{PTD}}{C_{s}^{PBU}}\right)\cdot 100 \tag{19}\]  
Despite the bottom-up approach presenting (for all four regions) a better average LRP for the links than the top-down approach, the gains in the number of CPs effectively selected to install routers and gateways reflect cost minimization. In addition, the top-down approach assures average LRP values that qualify the links in a high-quality (with \(LRP\geq l_{min}\)) threshold. Considering the gains in the number of CPs (15.3% for Region A,