# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
## 5 Performance Evaluation
### Simulation Results and Discussion  
In this section, the simulation results are summarized and compared with those of the conventional PRM and ABC algorithms. The simulation is performed in a 3D scenario. The UAV uses two transceivers operating concurrently. Our primary aim is to reduce the network delay by the minimal usage of the sensor's energy to enhance network performance. In the simulation, the UAV receives information regarding the CHs and their locations while it is moving. The UAV's path encompasses all the sensors while it moves within the WSN area. The UAV collects data from the CHs while it is moving or hovering to reduce packet loss. We have optimized the height, speed, threats, and flight time to enhance the proposed system's performance. The scenarios and results of the PRM implementation are shown in Figure 4. In Figure 4, PRM is implemented with a single static obstacle. The shortest collision-free roadmap designed by PRM is also presented aside. Likewise, in Figure 4, the simulation is done in the scenario with multiple obstacles, and the shortest path is designed. The roadmap acquired through the PRM algorithm in Figure 4 is used as the proposed HPP input. Our simulation is carried out with a varying number of static and dynamic obstacles, and the obtained results are shown in Figure 5. In Figure 5, we use a static obstacle and four dynamic obstacles. The proposed algorithm finds the way to the target area, avoiding all the obstacles. In Figure 5, two static obstacles and six dynamic obstacles are used. We consider dynamic obstacles of the same shape and size, which are randomly generated during the simulation. With the increase in the number of static and dynamic obstacles, we can notice that both the number of iterations required and the time taken to reach the goal point increase to some extent. Thus, our proposed algorithm performs well to find the shortest and collision-free path in a dynamic environment with sudden threats and obstacles. Figure 4: UAV’s path using PRM algorithm in two different scenarios: (**a**) single static obstacle and (**b**) multiple obstacles. As shown in Figure 6, the average time required by the UAV to encompass the assigned path is calculated. For the simulation, we have focused on the UAV's time to traverse the WSN deployed area. We have also compared our results with those obtained using the classical PRM and ABC schemes. The results validate that the proposed hybrid method optimizes the UAV's total flight time for a specified simulation time and condition. The reduction in flight time is due to the optimized UAV height and speed and the proposed algorithm's high convergence rate. The lifetime of the proposed UWSN is calculated and analyzed for all rounds. The simulation is performed for the specified number of rounds to verify the number of alive nodes. We have considered hybrid MAC and clustering for all three algorithms. The number of live nodes is increased for all three algorithms compared to the conventional PRM and ABC algorithm due to the hybrid MAC and clustering. Figure 7 shows the lifetime of the three path planning algorithms. It is apparent that the number of live nodes is proportionally low for the ABC and PRM schemes. However, for the proposed hybrid scheme, the number of living nodes is higher. This is due to the optimized hybrid path plan algorithm. Furthermore, the CH wake-up schedule is synchronized with the UAV flight  
Figure 5: UAV’s path using the proposed HPP algorithm with static and dynamic obstacles: (**a**) with single static obstacle and four dynamic obstacles and (**b**) multiple static obstacles and six dynamic obstacles. Figure 6: Average flight time of UAV in the network. time. Using this mechanism, the CH need not remain active for a longer duration, reducing energy consumption. This significantly affects the energy efficiency of the network. The WSNs and UAVs' energy consumption remains a design concern, as a reasonably long network lifetime is desirable.