## 5 Performance Evaluation
### Evaluation Results
Figure 4 and Table 4 display the number of unscheduled UAVs and their standard deviations \(\sigma\), a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance, in Scenario 3. In the early part of the simulation results, with \(\text{JSI}=0.75\) (higher JSI), fewer UAVs are unscheduled (in other words, most of the UAVs are scheduled), owing to the few edges in its corresponding conflict graph. In contrast, for the simulation results with JSI \(=0.125\) (a smaller JSI), its associated conflict graph is densely connected. Therefore, more UAVs are unscheduled when compared to the case of a higher JSI. The number of unscheduled UAVs increases in the later part of the simulation due to battery limitations. To maintain the number of scheduled UAVs as uniformly as possible during the simulation, the results with lower JSI present better performance than those with a higher JSI. A smaller standard deviation in Table 4 means the number of unscheduled UAVs (i.e., the UAVs with deactivated CCTV cameras) that are not changing dramatically. Therefore, the number of scheduled UAVs becomes stable; eventually reducing the chances of surveillance performance degradation. Moreover, the stable results show that the proposed algorithm is superior in  
Figure 3: Ratio of UAV-covered surveillance area and cumulative saved energy of unscheduled UAVs for the proposed EE algorithm (\(\text{JSI}=0.5\) and \(\text{JSI}=0.9\)), and BB algorithm where moving UAV (M-UAV) \(=5\) and moving UAV (M-UAV) \(=10\) (Scenario 1: 28 fixed UAVs (F-UAVs) and Scenario 2: 45 fixed UAVs (F-UAVs)). (**a**) Ratio of UAV-covered area (Scenario 1). (**b**) Ratio of UAV-covered area (Scenario 2). (**c**) Cumulative saved energy (Scenario 1). (**d**) Cumulative saved energy (Scenario 2). terms of energy-efficiency that is beneficial for extending surveillance lifetime and maximizing the charging energy. Figure 5 presents the trade-off between the ratio of UAV-covered surveillance area and the amount of saved energy of the EE algorithm in Scenario 3. The ratio of the UAV-covered area is compared to the ratio of the area at the initial states, where all of the UAVs are scheduled. In the corresponding setting, the minimum and maximum JSI are 0.125 and 1, respectively. Here, the appropriate JSI setting plays a key role in achieving better performance. In order to guarantee a wider surveillance area coverage, a higher JSI is used to put more weight on the ratio of the UAV-covered surveillance area and a lower weight on the queue stability (i.e., related to energy status). In contrast, the lower JSI leads to the reduction of the ratio of the UAV-covered surveillance areas, whereas it is beneficial in efficient aspects. The optimal JSI value depends on the real-world system parameter configurations (e.g., the number of charging towers, the number of UAVs, and the amount of energy that can be charged in each charging panel) and quality-of-service (QoS) requirements (e.g., target monitoring area and target saving energy). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Proposed EE Algorithm**} & \multicolumn{6}{c}{**BB Algorithm**} \\ \hline
**JSI** & **0.125** & **0.25** & **0.375** & **0.5** & **0.625** & **0.75** & **0.875** & **1.0** & \\ \hline \(\sigma\) & 4.564 & 4.900 & 5.001 & 5.798 & 6.783 & 8.054 & 8.814 & 9.387 & 9.162 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Standard deviation (\(\sigma\)) of the number of unscheduled UAVs for both proposed algorithms, EE and BB (Scenario 3: 20 fixed UAVs (F-UAVs), 10 moving UAVs (M-UAVs)). Figure 4: (a) EE algorithm, JSI \(=0.25\). (b) EE algorithm, JSI \(=0.5\). (c) EE algorithm, JSI \(=0.75\). (d) BB algorithm. The number of unscheduled UAVs for the proposed EE algorithm (JSI \(=0.25\), JSI \(=0.5\), and JSI \(=0.75\)), and BB algorithm (Scenario 3: 20 F-UAVs, 10 M-UAVs).