## 5 Simulation Results and Discussion
This ensured that at each iteration, the particles tried to minimize energy consumption and flight risk, while maximizing the SAI value, which proves the effectiveness of our proposed algorithm. The optimization performance of the path planner, in terms of energy consumption and flight risk estimation, can be expressed as a fitness function that indicates the effectiveness of path planning. Fitness function itself consists of two parts, (i) objective value, (ii) constraints value. An optimal path should not have any kind of constraints in its path planning, thus, all constraints should be zero. Figure 5a, demonstrates the effectiveness of our paths where all the constraints are zero. Figure 5b,c show the objective value, and total fitness value, respectively for the different part of fitness values in each waypoint. The SAI weight value has an impact on the total fitness value. Therefore, to find the necessary optimal weight value for SAI we conducted various simulations, as shown in Figure 6. As the weight value of SAI was expected to be positive, we selected a wide range of positive values for our simulation. For a large value of the weight, the impact on EC, FRE, and SAI was less. Therefore, we selected the weight value from a minimal positive value. The weight values and corresponding impact on EC, FRE, and SAI, along with objective values, are shown in Table 4.  
Figure 4: Optimal objective values, (**a**) energy consumption, (**b**) flight risk, and (**c**) SAI value. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Name** & **Conventional PSO** & **PSO with Modified Parameters (mPSO)** \\ \hline Inertia value & 0.7298 & 0.8 \\ \hline Personal Cognitive value & 1.4960 & 2.0 \\ \hline Social Cognitive value & 1.4960 & 2.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between Conventional PSO and m-PSO. In our implementation, a full area coverage was based on distributed path planning. We considered two scenarios of full coverage, with overlapping, and without overlapping, respectively. In overlapping conditions, to cover the whole surveillance area, UAVs take so many steps that are known as waypoints. Sometimes, it takes very high computational time to complete the task. On the other hand, the second scenario is a non-overlapping condition, which requires less computational time and converges faster. To make the environment less complicated and faster and more convenient to converge, we only considered the second scenario, in this study, which involves path planning with the non-overlapping condition. Figure 6(a), shows the optimal paths, followed by UAV1, UAV2, UAV3, and UAV4 started from the GBS set as the starting point and denoted by a green rectangular box. After surveilling of the whole area, it again came back to the endpoint, marked as a yellow rectangular box. In Figure 6(a), the red rectangle box represents the restricted areas where UAVs are not allowed to fly. With the edge cells having higher SAI value, UAVs need to surveille those edge areas first having level-3 importance, then the inner areas having \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Weight Value**} & \multirow{2}{*}{\begin{tabular}{c} **Energy** \\ **Consumption** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Flight Risk** \\ **Estimation** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Surveillance Area** \\ **Importance** \\ \end{tabular} } & \multirow{2}{*}{**Objective Value**} \\ \hline
1 & \(-\)0.3619 & \(-\)0.6356 & 0.4597 & \(-\)0.5978 \\ \hline
2 & \(-\)0.3577 & \(-\)0.6186 & 0.9096 & \(-\)0.0567 \\ \hline
3 & \(-\)0.3478 & \(-\)0.5939 & 1.6792 & 0.7178 \\ \hline
4 & \(-\)0.3789 & \(-\)0.5705 & 2.2389 & 1.2775 \\ \hline
5 & \(-\)0.3652 & \(-\)0.5937 & 2.7986 & 1.8297 \\ \hline
6 & \(-\)0.3580 & \(-\)0.5655 & 3.3583 & 2.3848 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Objective value varying SAI weight value.