## 5 Conclusions  
The heuristic proposed allows practitioners to deploy the necessary number of UDAPs for the monitoring, supervision and control of conventional electrical network, providing coverage to a number \(n\) of SMs and making possible the integration of microgrids with the conventional electrical system. In this way, final users of energy resources will become consumers and prosumers thanks to the integration of DRES. A fundamental feature of the model is that it adapts to the conditions of the required wireless network. In addition, the research carried out allowed us to determine the importance of reducing to the maximum the end to end delay of the entire network. This metric not only provides information in terms of time, but in addition, allows us to comprehend and minimize the chargeability of the network and the need to allocate the capacity of the point-to-point links for its efficient operation. The model has been shown to be scalable in time and space and has the following characteristics: presents finite solutions and optimizes the resources required by the FiWi network using an efficient clustering method (different to the traditional). Moreover, with the N-NST algorithm, balanced clusters can be built, which are subject to real restrictions, such as capacity and coverage. The heuristics works with georeferenced scenarios, reducing to the maximum the aggregation delays of data of each cluster using the ODB algorithm. Furthermore, it minimizes FSPL and is a planning model of NP-complete complexity. The complexity of the problem lies in the population density of SMs, since, in a graph with \(n\) SMs, there are \(n^{n-2}\) possible trees; thus, the proposed model is combinatorial in nature. Hence, the results obtained are near optimal due to the exponential increase in the complexity if there is a minimal increase of the SMs in the scenario.  
Consequently, in order to relax the problem, stop criteria are introduced. The goal is that once the algorithm converges, it stops providing a near optimal solution. We assume that all the nodes are linked by cellular technology, a very expensive situation. As the model replaces the cellular links with lower cost WiFi links, the objective function decreases as much as possible, thus approaching the optimal solution. Once the model cannot further decrease the cost, the algorithm stops. Therefore, the objective of this research is to minimize cellular links and to maximize WiFi links guaranteeing coverage to the nodes located in the area of interest. Another fundamental characteristic of the present model is its combinatorial nature; because, if the density of nodes increases and due to the capacity and coverage restrictions, the nodes are not covered, and after verifying the best options, these nodes must necessarily be UDAPs and could serve as future expansions.  
In future works, a comparative analysis will be carried out between different clustering methods. The link capacity restriction (Mbps) will be increased to decide on the topology, and finally, the fault tolerance will be included, as well.  
Figure 6: Average links crossed by a data packet \(L\) = 800-bit, Lambda = 0.1 package/s. Source: the authors.  
* [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] carried out the experiments and analyzed the data. [PERSON], [PERSON], [PERSON] and [PERSON] contributed the preparation of manuscript.
* This research received no external funding. The APC was funded by Universidad Politecnica Salesiana.  
**Conflicts of Interest:** The authors declare no conflict of interest.