## 4 Results  
The near optimal route map on an advanced measurement infrastructure under the concept of FiWi network allows analysts to know the state of the conventional electrical network for the optimal integration of microgrids and is presented in Figure 2. By having a georeferenced route map, we have all the information required to run the actual deployment, and more importantly, we can account for each of the resources required for planning, implementation, economic assessing and FiWi network operability. In Figure 2 is depicted the existence of a multi-jump intra-cluster, for securing 100% coverage of each of the SMs in the area of interest. It is very important to point out that each cluster of the present paper is formed with a method that is different from the conventional clustering methods (k-means, k-medoid and mean shift). The method that was developed to achieve the goals of the research proposes the application of Algorithm 1, N-NST. Since it is capable of forming balanced clusters, subject to restrictions, it allows us to build clusters of similar lengths, contributing in this way to reliable data on each cluster. With it, it is possible to make a sound planning with the respective analysis, which is part of a tree-type wireless hierarchical network. It is known that the above-mentioned conventional algorithm uses divisive methods to form clusters without observing the lengths of each one. Therefore, they are unpredictable and do not build balanced conglomerates. In addition, they are not able to accept design parameters such as: capacity and coverage. In Figure 3, we can identify the near optimal route map accompanying the respective sparsity pattern matrix (spy) obtained from the binary array of dispersed adjacency of length \(n\times n\). Therefore, using these square matrices, the binary relationships one and zero are represented, where one represents the existence of an edge and zero its non-existence. For each node, which binds to an edge, is placed a one represented in blue in Figure 3, and in the remainder is placed a zero represented in white color. Therefore, spy is a binary matrix that contains the information of the vertices and edges of the solution to the problem posed in this research. In this figure is proposed a scenario defined by a finite number of nodes, in which two different criteria of selection of the UDAP are applied. Figure 3a,b corresponds to the first criterion of the selection of the UDAP, that is by the minimum distance from the closest BS to one of the SMs of the corresponding cluster. The SMs that meets this condition will be selected as UDAP, and the rest will be single-access SMs to WiFi technology. Figure 3c,d corresponds to the second criterion, which applies the ODB algorithm for the selection of the UDAP. The characteristics of the sparsity pattern matrix in this research are: square matrix, binary, symmetric and the inputs of the zero diagonal. If the diagonal is zero, this is because there cannot be one edge of one vertex and toward the vertex \(v\), since it will be the same vertex and it is not possible to construct a graph \(G(V,V)\). Therefore, a graph is defined as \(G(V,A)\), where \(V\) is the vertex represented by SMs, UDAPs, BSs or the central office, and A are the edges represented by the WiFi-cellular links that provide a link address, in such a way that a direct graph will be built. Therefore, the spy matrices in Figure 3 represent the connectivity array from a vertex \(i\) to a vertex \(j\), denoted as _Vij_. The number of nonzero elements of the spy arrays is 988 (see Figure 3), which divided to two, results in 494, which is the number of WiFi links required by the network, which represents 96.48% of the use of technology with cost \(C_{2}\) and  
Figure 2: Near optimal deployment of SMs using Fiber-Wireless (FiWi) network. Source: the authors. 3.52% of cellular links at a cost \(C_{1}\) for hybrid wireless communication.