## 4 Experiments and Results
### Models of Process of the Capacities of Self-Optimization and Self-Configuration  
The Self-optimization capacities and Self-configuration have a strong correlation, in other words, the self-configuration method is complemented with the self-optimization method, while a capacity configures the parameters according to the network's scale, the other optimizes the values to be coupled in the protocols' parameters OLSR and AODV as an extension to the same ones. To implement the model of self-optimization process, it modified the original modules of the protocols OLSR and AODV (aodv_rte.pr.c and olsr_rte.pr.c) that take part of the support for networks MANET of the simulator OPNET and it was added to the self-optimization function and self-configuration. Countless modifications were made in the structure of the modules with intention to adapt those capacities. The self-optimization method is responsible for the choice of the value optimized to be put in each parameter of the routing protocols OLSR and AODV. To obtain the optimized value, a network was simulated in WMN Intra-campus as the scenario presented in the Figure 5. In this scenario the variation of the amount of nodes clients was simulated and associated in each node gateway, together with the variation of the interval of the chosen parameters for this experiment. In Figure 4, an example of the curve behavior can be observed created through the relationship throughput versus variation of the time of messages Interval HELLO. In this example we noticed that the analysis of the variation of parameter's value was made in an independent way for the three proposed scales, small, medium and large. We can also see that the proposal objective function adapted from a satisfactory way to the graphs of the Figure 4, resulting in a optimized value for each proposed scale. Regarding to optimization process of the generated curve, we choose to use the software MATLAB with the optimization function _lsqnonlin_, this function is used to solve adjustments no linear problems, and it was used in this work to aid in the adjustment of the curve created by the specific analysis of each parameter. According to above mentioned in the Section 3.4, the hyperbolic, cotangent function adapt to the behavior of the generated curve. With the obtained data of the Table1 the curve was drawn in the three scales and, after that, it was researched an objective function in the literature that was better adapted to the behavior of the generated curve. After tests and analysis in the simulator, the hyperbolic cotangent function was chosen, because this obtained the best behavior among the other objective functions analyzed (linear and tangent interpolation hyperbolic). To exemplify the analysis process the parameter Hello-Interval was used as reference, but the same process was made for the other chosen parameters. As shown in the Table 1 the example of the data can be seen as only obtained of the parameter Hello-Interval. Still in this example, it is noticed, that for the analysis of Hello-Interval the interval of time of 0.5 was varied up to 50 seconds and also the amount of node-clients that was varied the scale of the network accordingly: SMALL (0 to 30), MEDIUM (30 to 70) and LARGE (70 to 100). In the Tables 2 and 3 the parameters of the protocols can be visualized OLSR and AODV after the self-optimization process. With intention to analyze the parameters in a specific way, five parameters of the protocol AODV were chosen (Route-Request-Retries, Hello-Interval, Active-Route-Timeout, Allowed-Hello-Loss, Net-Diameter) and five parameters of the protocol OLSR (Hello-Interval, Tc-Interval, Neighbor-Hold-team, Tc-Hold-team, Duplicate-Hold-team). This analysis was accomplished being varied the interval of time of each parameter and analyzing the growth of the network according to the scales. Therefore was used the throughput metric of the network.