## 5 Results
### Analysis of the Effect of the Parameters on the System's Performance  
Once all of the figures of merit have been obtained, we can analyze the effect of each parameter in the outcome of the fuzzy control-based self-adaptive system. This final analysis is performed applying Equations (21) and (22) to the values obtained in the experiments in correlation to the grouping of the parameters' set. \[SN_{p,v}=\frac{\sum_{n=0}^{N_{v}}J_{p,v}}{N_{v}} \tag{21}\]  
\[\Delta J_{m}=max(SN_{p,v})-min(SN_{p,v}) \tag{22}\]  
For instance, the estimation of the effect of each configuration parameter on the energy consumption, using the figures previously published in [10], is shown in Table 4. In our case, each configuration parameter can have two potential values. The \(ND_{R}\) can be either two or three; the \(\xi_{ND}\) can be zero or one; and the \(k_{CR}\) can be one or three. Therefore, in this case, the first row, corresponding to the parameter level \(SN_{p1}\), shows the sum of the figure \(J_{e}\) values when the first value of the configuration parameters \(ND_{R},\xi_{ND}\) and \(k_{CR}\) is used, divided by the number of times it is used (in this case, four). The second row shows the same for the second possible value for each configuration parameter. The final row shows the result of applying Equation (22) to the previous rows. The greatest value in this row identifies which parameter has the greatest effect on this figure of merit. In this case, the tolerance represented by \(\xi_{ND}\) is the parameter that, according to this estimation, has the greatest effect on the energy consumption for the whole network, representing 47.02% of the total estimation of the effect. The \(k_{CR}\) factor represents 36.08%, and the lowest impact on the energy consumption is represented by the \(ND_{R}\), with 16.90% of the total estimation. The same procedure is used to estimate the effect of each parameter on the connectivity, using again the figures from [10], as shown in Table 5. This time, the configuration parameter more relevant to the connectivity is the scaling factor \(k_{CR}\), with 61.06% of the total estimation. The next parameter in relevance is the \(ND_{R}\), with a 37.69% representation. Additionally, the smallest impact on the final result is due to the tolerance parameter \(\xi_{ND}\), with 1.24%. Finally, we make the proper estimation for the dynamics presented in this paper, focusing just on the transmission power dynamics, as that is the only dynamic on which the self-adaptive system acts. The results are shown in Table 6, and the tolerance represented by \(\xi_{ND}\) is the parameter that has the greatest impact on the transmission power dynamics, representing 78.57% of the total estimation. Next follows the \(ND_{R}\) parameter with 18.65%, and the smallest impact is caused by the \(k_{CR}\) factor, with 2.78% representation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Level** & \(ND_{R}\) & \(\xi_{ND}\) & \(k_{CR}\) \\ \hline \(SN_{p1}\) & 3865.4525 & 3895.2625 & 3884.5525 \\ \(SN_{p2}\) & 3832.0125 & 3802.2025 & 3812.9125 \\ \hline \(\Delta J_{e}\) & **33.44** & **93.06** & **71.4** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation of the effects of each input parameter for energy consumption. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Level** & \(ND_{R}\) & \(\xi_{ND}\) & \(k_{CR}\) \\ \hline \(SN_{p1}\) & 25.8917 & 21.0376 & 29.0041 \\ \(SN_{p2}\) & 15.8518 & 20.7059 & 12.7394 \\ \hline \(\Delta J_{e}\) & **10.0398** & **0.3317** & **16.2647** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation of the effects of each input parameter for the connectivity. As a summary, we can conclude that the parameter \(\xi_{ND}\) is the most relevant for the proposed fuzzy control-based self-adaptive system, being the one with higher impact on the energy consumption and the communication range dynamics, although it has the lowest effect on the connectivity.