## 4 Results
As a result, the heuristic has been able to provide a minimum route map, required for the planning of a hybrid FiWi network at the lowest cost while maximizing reliability and the robustness of the bidirectional communication network needed to control and supervise the conventional electrical network allowing us by optimal information management to integrate SMG systems that will be able to run connected to the network through an adequate synchronization and, in the same way, able to work in islanded mode, namely disconnected from the system. The importance of microgrids, through an adequate two-way communication system, is that they can operate autonomously according to what the physical and economic conditions dictate. Figure 4 shows the increases in end to end delays as the capacity of a UDAP to accommodate SMs increases. This happens because the ability to agglutinate a cluster is directly related to the number of average links that a data package must go through to transmit the package from the SMs to their respective UDAPs. In addition, the higher the capacity of the UDAP, the more various effects may occur, such as: increased delay time in collecting the information, greater distances of transmission, greater number of jumps and greater chargeability of each link in the network. On the other hand, in each density of SMs, the topologies of each cluster are changing, to comply with the requirements of the network, which causes and requires different routing characteristics to the extent that the density of SMs is increasing or decreasing, causing variability in the features of each cluster and therefore the resulting topology. As a result, if clusters are built with minimum distances, the need to transmit through multiple jumps is null. Therefore, the delay is directly proportional to the capacity-coverage of the UDAP and inversely proportional to the density of the SMs. Figure 4: End to end delay generated by each population increase by varying the capacity of each cluster with traffic 0.1 package/s, \(L=200\) bits. Source: the authors. In Table 5, the capacity algorithm is presented 2. This algorithm helps to reduce the average times in which the UDAP collects the information from the group. The ODB algorithm performs intra-cluster scans to determine the best concentrator position (UDAP). It can be seen that by increasing the density of nodes (SMs) and maintaining the capacity of the conglomerates, the need to deploy UDAPs also increases. The number of UDAPs required in each scenario is different since a heuristic has a stop criterion. Therefore, once the restrictions are met, the algorithm stops providing one of the possible combinations as a solution that satisfies the constraints of the problem. Moreover, the model being combinatorial and having complexity that is NP-complete only provides solutions that are close to optimal. Hence, it would demand an excessive computational time to explore each of the possible combinations and, thus, to determine an optimal global solution. Consequently, the stopping criteria (restrictions) contribute to the relaxation of machine time that the heuristic takes to provide a near optimal solution. Finally, Table 5 shows the end to end transmission percentages that can be reduced by applying the ODB algorithm to a previous solution. Figure 5a shows the metric obtained with the following characteristics: data length \(L\) = 800 bits, Lambda = 0.1 package/s and by varying the density of SMs and the capabilities of each cluster. In Figure 5b, \(L\) is kept, the density of SMs is \(n\) = 512 and Lambda and the capabilities are varied. In Figure 5a,b, it is noted that, when the need of UDAPs decreases, the average delay of the entire wireless network increases. This happens due to the increase in the capacity of each UDAP to accommodate SMs. If the capacity to accommodate SMs of a UDAP increases and its radio coverage is minimum, the need for multiple jumps to aggregate data from the more distant nodes to the UDAP also increases.