# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
## 5 Performance Evaluation
### Simulation Results and Discussion
The average energy consumption of the UAV and SNs are calculated and analyzed, as shown in Figure 8. We have compared the obtained results using the energy consumption of the PRM and ABC algorithms. From the results shown in Figure 8a, it is apparent that the energy usage of the UAV decreased when using the proposed hybrid path planning method. We have focused on the energy consumption of the UAV during its flight and communication with the SNs. Minimization in energy consumption is possible owing to the optimization process and reduced flight time, as shown in Figure 6.  
As shown in Figure 8b, we calculated the average energy required by any CH to communicate with the UAV. We ran the simulation for the specified number of rounds and then calculated the average time of CHs per round. From the figure, it is evident that the energy consumption of the CHs in the proposed algorithm decreased slightly in every round of UAV-to-CH communication. This significantly helps to reduce the energy consumption of the CHs when it is operated for a more significant number of rounds. Consequently, the lifetime of the overall network can be improved. Figure 9 shows the total distance traveled by the UAV to encompass the sensor deployed region. The results show that the proposed method's distance is minimal for the three simulated methods. The reduction in flight path is because the HPP uses the shortest  
Figure 8: Energy consumption. Figure 7: Network lifetime. path provided by the PRM unless it encounters a collision and obtains an optimized path provided by the ABC to manage the complex environment's different constraints. Initially, the PRM path planning performs well; however, it cannot handle the dynamic obstacles, and the height and speed are not optimized. We aim to minimize the flight distance to complete the mission quickly to lessen the damage in emergencies. Figure 10 shows the PDRs of the PRM, ABC, and the proposed algorithm. According to the results, the proposed algorithm's PDR remains higher than 80% after the UAV entered the WSN region. The change in packet loss from the proposed method is minimal due to the speed and height adaptation during the data collection time. Additionally, UAV hovers when data collection is not completed. The proposed method is then compared with the PRM and ABC algorithms. From the comparison, we can conclude that the number of packets received from the proposed algorithm is higher than those from the PRM and ABC algorithms. The PRM shows the lowest packet delivery rate because it does not function appropriately in complex environments and adapt in accordance with the constraints. We used the optimized ABC algorithm for the local path plan because of its fast convergence capacity. We evaluated the convergence time of the ABC algorithm and compared it with other widely used path planning algorithms (i.e., RRT, GA, and DP) in Figure 11. We used the same network configuration for all the algorithms used in the comparison. Based on the obtained results, it is apparent that the convergence time of HPP is the shortest for almost all the rounds. In our proposed HPP algorithm, the classical  
Figure 5.0 Packet delivery ratio. Figure 5.1 Total distance encompassed. ABC algorithm is modified by optimizing various constraints. As a result, the proposed algorithm offers a faster convergence speed than the other three popular algorithms.