## 5 Performance Evaluation
### Evaluation Results  
Figure 3 presents the ratio of the UAV-covered area (i.e., surveillance enabled area), which is compared to the ratio of the total area of the Manhattan map. The cumulative saved energy denotes the hovering and communication-related energy for unscheduled UAVs. The connectivity of the conflict graphs updates according to the overlapping threshold setting, associated with the number of reference points in overlapping areas, calculated by Jaccard similarity index (JSI) in this paper, as presented in (13). The lower JSI increases the number of edges in the corresponding conflict graph. Subsequently, relatively small numbers of UAVs are scheduled. On the other hand, a high JSI decreases the number of edges in the given corresponding conflict graph. Therefore, a relatively substantial number of UAVs can be scheduled to suspend surveillance, which can reduce the overall hovering energy consumption. Consequently, we consider various JSI settings to numerically estimate the corresponding effects. Our proposed algorithm is named EE, which stands for energy-efficient UAV scheduling as compared to the baseline algorithm is noted as BB for battery-based algorithm. In Figure 3, the ratio of the covered area decreases in Scenarios 1 and 2. This is because the residual batteries of UAVs become low, resulting in numerous UAVs needing to be charged as time goes by. Nevertheless, the EE algorithm reduces the performance degradation of the monitoring area through the efforts of the MWIS-based UAV scheduling compared to the BB algorithm. In Scenario 1, the deployment scenario has few overlapping areas, and each UAV plays a key role due to sparse deployment. Thus, the results reveal that it has more drawbacks in Scenario 1 in terms of coverage performance than the performance in Scenario 2. To compare the two algorithms, the EE algorithm tends to gradually decrease the surveillance area, whereas the BB algorithm sharply decreases the surveillance area in Scenarios 1 and 2. The cumulative saved energy is related to the number of unscheduled UAVs. The proposed EE algorithm with JSI = 0.5 is superior to the BB algorithm in the view of energy savings for the first 40 min. In addition to this energy efficiency, it also has better performance in terms of surveillance coverage ratios. In the last 10 min., the BB algorithm has a poorly \begin{table}
\begin{tabular}{l c} \hline \hline
**UAV Parameters** & **Value** \\ \hline Aircraft size (L \(\times\) W \(\times\) H) & 289.5 mm \(\times\) 289.5 mm \(\times\) 196 mm \\ Aircraft weight & 1375 g \\ Flight speed (max) & 20 m/s \\ Field of view & 84 degree \\ Flight time (max) & 30 min \\ Capacity of flight battery & 5870 mAh \\ Charging power of flight battery (max) & 160 W \\ Voltage of charger & 174 V \\ Rated power of charger & 100 W \\ Charging efficiency loss & 1.1 \\ \hline \hline
**System Parameters** & **Value** \\ \hline Size of Manhattan map & 1299 m \(\times\) 750 m \\ Number of reference points & 1479 \\ Number of UAVs in Scenario 1 & F-UAVs: 28. M-UAVs: 5 and 10 \\ Number of UAVs in Scenario 2 & F-UAVs: 45, M-UAVs: 5 and 10 \\ Number of UAVs in Scenario 3 & F-UAVs: 20, M-UAVs: 10 \\ Altitude of surveillance UAVs & 100 m \\ Number of charging towers & 4 \\ Number of charging panels in the charging tower & 4 \\ Number of charging panels in the ground station & 50 \\ Simulation time & 60 and 120 min \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Environmental setup parameters. covered area of less than half of the total area; thus, the results of energy efficiency are meaningless. Lastly, in each scenario, 10 M-UAVs improve the coverage and energy performance in comparison with five UAVs. A larger number of UAVs create more edges in the given corresponding conflict graph; thus, our EE algorithm provides better performance in every case.