## 5 Analysis of the Results
It is necessary to establish a capacity parameter greater than or equal to 15 users and a reach greater than or equal to 75 m.  
From the geo-referenced nodes, the result of the final topology considering Prim and SCP is presented Figure 5 shows the result of a scenario whose capacity restrictions are 20 devices per node and a range of 75 m; here, it can be evidenced the topology with 174 nodes and the routes that have been formed. In addition, it also shows that out of 50 UDAPs deployed, 30 have been reduced. On the other hand, Figure 6, shows the result of another scenario, whose range is restricted to 100 m and capacity to 20 users per node; here, it can be seen that from the 50 UDAPs of the proposed case, 37 are eliminated, and with the resulting 13 a new routing with the other elements of the network is generated. Therefore the applied model confirms its performance in optimizing infrastructure resources. Figure 3: Number of users per UDAP vs. Number of UDAP. Employing the wireless network simulator QualNet Developer 5.2, the topology of the Zigbee network of Figure 7 obtained from the Matlab optimization algorithm is  
Figure 4: Percentage of devices covered—Greedy SCP. Figure 5: Topology with capacity of 20 users per node and range of 75 m.  
Figure 6: Final network topology of Scenario 4 with capacity of 20 users. implemented, on the other hand, another WiFi scenario is configured with the same number of nodes, but with other configuration parameters; for both cases, the geographical location of the devices in the respective canvas is considered. A difference between the two technologies to be tested in WiFi and 19 local networks has been configured; conversely, in Zigbee, a single mesh network is used between the UDAPs and SMs of the infrastructure. Table 4 summarizes the configurations of the scenarios, taking into account that 156 users, two central offices, three base stations, in addition to 13 UDAPs for WiFi and 20 UDAPs for Zigbee are established. In WiFi, each UDAP functions as an access point, while in Zigbee, each UDAP is an area coordinator, and a central office acts as the general coordinator of the network. In the case of WiFi, the 2400 MHz band is used, as it is openly available for the region, while in the case of Zigbee, it conforms to the Australian standard, which sets the 915 MHz band. Another difference between the two scenarios is the propagation model because Okumura-Hata is adapted to the frequency band that Zigbee operates in this part of Australia, while Two-Ray is coupled to the frequency band in which WiFi operates. The other configurations are also determined based on the technology in use for each scenario. For the two scenarios to be simulated, many data packets are generated and sent between some nodes to test the performance in the transfer of information. Therefore Table 5 is presented, which indicates the constant bit rate in WiFi technology for chosen nodes; from this table, there is some packets, their corresponding weight, and the sending time interval to obtain; as a result, a total of data sent in the established time from the beginning to the end. Furthermore, It allows to verify that n some nodes queuing will be generated due to the amount of simultaneous incoming and outgoing data; therefore, by applying the first-in, first-out (PEPS or FIFO) method, it will be possible to establish which of the incoming data can be sent out and which are dropped. Similarly, the information to be tested for the Zigbee scenario can be verified in Table 6.