# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 6 Discussions and Future Work  
In this section, we review the main objectives of the work and evaluate the results obtained from the research. The main research objectives include the following:  
* The presentation of an efficient AMI planning method with a large-scale focus. * The use of a propagation model that does not depend on an empirical terrain classification. * The use of a heuristic approach based on a spanning tree and clustering, capable of evaluating a smaller number of connections and resulting in topologies that use fewer routers and gateways. Regarding the applicability of the method in large-scale scenarios, experimentation with real data from regions of Brazil totaling more than 230,000 smart meters demonstrated that the proposed method effectively deals with datasets from regions with different degrees of density of smart meters and poles, including high-density regions (such as dense urban regions) and regions with high sparseness (rural regions or industrial areas). As observed in Table 6, the processing times for the different real regions considered consumed from 14 min 19 s (for Region D) to 15 h 47 min 44 s (for Region A). The use of a path loss model that calculates losses considering the particularities of the terrain between the devices permitted the evaluation of the possibility of connection more precisely, with the advantage of visualizing the connectivity condition for the entire region (as can be seen with Figure 3). The average LRP values obtained in the processing of each region evaluated (varying from \(-\)79.14 dBm for Region B to \(-\)72.86 dBm for Region C, according to Table 6) show that the use of a detailed path loss model made it possible to achieve an optimized result. The implemented heuristic approach demonstrated efficiency for the different scenarios evaluated as the number of candidate positions calculated for each region was enough for the connection of the smart meters respecting the value of LRP established for the experiments. Using a grid strategy to select poles helps minimize the final number of installation positions and optimizes the method's performance by decreasing the number of connections to be evaluated. For example, in Table 5, it is possible to observe that Region A has 62,412 poles. Despite this, we observed (Table 6) that the total number of poles selected  
Figure 11: Average number of smart meters (SMs) connected to each CP selected by the AIDA method using its original path loss model (AIDA/proposed PL model) compared to the values connected to the CPs 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. for region A (for the installation of routers and gateways) is only 249 poles (using the top-down clustering approach). It is important to highlight that, for Region A, the process of candidate position calculation establishes 1245 poles to be evaluated (163 poles for the first iteration, 289 poles for the second iteration, and 793 poles for the third iteration of the method). Despite the region having 62,412 poles, only 1245 pole positions (1.995%) were evaluated to establish the connectivity of the meters in the region. This type of simplification reinforces the method's applicability for large-scale scenarios since there is a significant decrease in the number of calculations performed with the implemented strategy. The multihop analysis for connecting meters using a minimum spanning tree algorithm also helps reduce the complexity of the problem and reduce the calculations to be performed, as only a subset of possible connections between smart meters needs to be evaluated. Regarding scalability, it is possible to highlight that the method worked well regarding the processing time, reaching shorter execution times when compared to usage scenarios in which it was considered a simplified path loss model.