## 4 Results
If we checked Scenario 1 in Tables 3 and 4, we can identify that we need 494 WiFi links and 18 UDAPs, giving as a result \(n=512\), which is the number of SMs to deploy in \(A(n)\). Accordingly, the number of nonzero elements of the spy arrays of Figure 3 corresponds to the set of vertices and edges \(V_{ij}\) and its respective image \(V_{ji}\), after which being added, we have \(V_{ij}+V_{ji}\), if \(V_{ij}=V_{ji}\); as we refer to the same link, the result is \(2V_{ij}\). Therefore, if we replace the required number of WiFi links from Scenario 1 of Table 3 in the previous expression, we are left with the number of nonzero elements \(nonzeros=2*494=988\), presented in Figure 3.  
Considering the above statements, in Figure 3b,d, completely different arrays can be seen, with the same number of nonzero elements, which correspond to the binary matrices resulting from adjacency by applying different criteria for the selection of the UDAP. In Figure 3b, greater dispersion of the nonzeros in the positions (400, 400) can be seen. Comparing this with Figure 3d, the existence of a greater number of jumps required to guarantee the coverage for each SMs available on the stage occurs; therefore, the dispersion is associated with the number of hops. Consequently, the end to end delay parameters and FSPL will be increased. In Figure 3d, through the application of the ODB algorithm, unnecessary dispersions are eliminated. Reducing the possible utilization of jumps to the maximum, to transmit data packages from the most distant SMs toward their respective UDAP, this contributes to a significant reduction of the delay for a UDAP to add and to merge the information of its associated clusters to relay to the respective BSs. In the same way, FSPL is diminished. In Figure 3, it can be determined that the SMs suitable to be selected as UDAP by the ODB algorithm are the nearest nodes  
Figure 3: WiFi neighbor adjacency matrix \(n=512\). (**a**) and (**b**) preliminary deployment, (**a**) route map and (**b**) representation of the adjacency matrix; (**c**) and (**d**) correspond to the scenario, minimizing the delays. Source: the authors. to the center of mass of each group. Thereby reducing the average end to end delay of each group to the maximum. This decreases the average number of links that a data package must pass through to reach the respective UDAP. If the number of crossed links increases, this is because the SMs are far away from their respective UDAPs and require mandatory jumps to be able to transmit. This can happen because the radio coverage of the UDAP does not guarantee observability of the furthest SM. Therefore, if the number of crossed links to transmit data packages from SMs until their respective UDAP increases, this is because in the same way, different variables increase, such as the distances of transmission and the jumps required, and consequently, end to end delay increases. Therefore, the end to end delay is directly proportional to the number of average links crossed by a data package. In addition, through Figure 3, it is shown that the heuristics proposed is able to mutate the adjacency matrix, seeking to provide the best resulting topology to the solution of the problem. The topology will ensure a significant reduction of the average end to end delay that the UDAP takes to add the information of its associated clusters. Therefore, in Figure 3c,d, the georeferenced near optimal deployment of SMs is shown. This serves for measurement, monitoring and control of the conventional electrical system, giving rise to the possibility of an optimal data management and the integration of micro-grids to increase the reliability and quality of energy. Table 3 presents the required number of links and the computation of the analyzed variables in this paper for the required wireless WiFi network. It presents five different scenarios, in which the density of SMs is varied \(n\) (512, 256, 128, 64 and 32) to be deployed in \(A(n)\), thus demonstrating the criterion of scalability enabled by the heuristic proposed.