## 4 Experimental Evaluation
### Results  
The choice of the configuration parameters for the studied scenarios has been driven by considering typical or reasonable parameters from a realistic point of view, as summarized in Table 2. With these values, the coverage area is \(R=40.3\) m.  
Firstly, we study the performance of the SA algorithm with different values for its configuration parameters: initial temperature (\(T\)) and the number of iterations. In general, a higher initial temperature increases the chances for escaping from local maxima, although an excessive temperature can drive the optimizer to escape from the global maximum. For that reason, setting the initial temperature is usually a delicate matter and depends on the concrete application. For the case of the number of iterations, and as a general rule, a higher number of iterations provides more opportunities to improve the solution, but it also increases the time that the algorithm is operating with high temperatures and, so, the risk of escaping from the global maximum (in addition to the increased computation cost, of course). In Figures 4-6, we show the variation of the utility and the confidence intervals at 95% of the final negotiation outcome for different values of temperature and the number of iterations for Scenarios 3, 6 and 9 (we omit the rest of the scenarios, as the results obtained are very similar). In those figures, we notice that, for the number of iterations, the best choice is to choose the highest number as possible to obtain higher utilities. This effect occurs in all of the studied scenarios (not only the ones depicted in the figures), with the exception of one of them (Scenario 6 in Figure 5 with \(T=8\)), where the utility function decreases from \(220.56\) to \(218.34\) for \(2000\) and \(3000\) iterations, respectively. Regarding the initial temperature \(T\), the best choice is \(T=1\). From this point on, the SA algorithm will be executed with \(T=1\) and \(3000\) iterations. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline \(P_{t}\) & \(30\) mW \\ \(G_{t}\) & \(0\) dB \\ \(G_{r}\) & \(0\) dB \\ \(L\) & \(40\) dB \\ \(S\) & \(-90\) dBm \\ \(h_{t}\) & \(1.5\) m \\ \(h_{r}\) & \(1.5\) m \\ \(\Psi\) (APs) & \(0.5\) \\ \(\Psi\) (C) & \(0.2\) \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of the parameters (C, camera). Figure 4: Simulated annealing (SA) evaluation in Scenario 3. Figure 5: SA evaluation in Scenario 6. Once the SA algorithm has been tuned, the next experiments are focused on determining the impact of having a different number of agents or network providers (\(p\)) in the negotiation. In Figure 7, we have evaluated all of the scenarios under study with \(p\in[2,5,10]\). From the figure, we can conclude that, as expected, increasing the number of providers \(p\) yields a progressive diminution of the global utility, since trade-off solutions involving access points in different providers will be more likely to be rejected. However, this reduction is moderate enough. From this point on, all of the results belong to the case \(p=2\). The reason for this choice is two-fold. First, we think it is reasonable to consider a small number of providers offering surveillance services. Second, there are more works in complex bilateral negotiations than for the multilateral case (three or more agents). Next, we compare the SA algorithm with the rest of the proposals: random channel assignment, SCS, HC and ALHSO. In Tables 3-5, we show the average (avg), standard deviation (SD) and confidence intervals at 95% (CI) of the utility function for 10 executions of the algorithm in each of the proposed scenarios. Furthermore, we highlight in bold the best solution. As can be observed, regarding the mean utility, the SA algorithm is the best solution for all of the scenarios, except for Scenario 8, where ALHSO shows the best performance. The second best performance is obtained by ALHSO, followed, by far, by SCS.