# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
## 1 Introduction  
In recent years, unmanned aerial vehicles (UAVs) have been used increasingly in many fields. UAVs can accomplish extremely difficult or dangerous tasks for human beings, such as firefighting, disaster relief, and search and rescue [1; 2; 3]. Owing to UAVs' ability to sense, monitor, and maneuver on a broader area within a short period of time, their use is becoming more popular in many applications [4; 5; 6; 7; 8]. UAVs have also played very significant roles in the face of the global COVID-19 pandemic. UAVs are used as a means of transportation to pick up and deliver test samples and medical supplies to minimize the risk of exposure and time. In many countries, UAVs are employed for the aerial spraying of disinfect in potentially contaminated places.  
Moreover, UAVs are suitable for use in wireless sensor networks (WSNs) as mobile sinks to collect data from sensors powered by limited energy and then transfer them to the base station (BS) [9; 10]. Typically, UAVs are incorporated in WSNs to minimize the energy consumption of sensor nodes. In most WSN applications, sensor nodes are deployed in areas where replacing or recharging the batteries of small and energy-constrained sensors is challenging or impossible. In our study, we used a UAV as a mobile data collector in WSNs in emergency environments. The emergency environments indicate post-war and post-disaster scenarios that lack a fully connected network of sensor nodes (SNs) [11; 12]. We cannot rely solely on arrangements built previously, as the established network might not remain or function [13]. The use of UAVs to support SNs is promising for infrastructure-less integration. Hence, UAV-aided WSNs (UWSNs) are investigated in this study to reduce the energy consumption of sensors [14] and minimize the network latency.  
In a dynamic environment, there may be obstacles to the accomplishment of different UAV missions. Thus, UAVs must have the autonomy to perform various operations using sensors, a global positioning system (GPS), and cameras [15; 16]. Path planning in UWSNs differs from path planning in pure UAV networks. A routing algorithm for beamforming-constrained swarm networking to improve the network capacity is studied in [17]. In [18], a distributed system to identify the appearance and the approximate position of unwelcome drones using a wireless acoustic sensor network and machine learning algorithms is studied. A lightweight blockchain-based secure routing algorithm for swarm UAV networking is proposed in [19]. Path planning in UWSNs aims to identify the optimal flight path from the starting point to the target point by considering the flying time, threats, and optimum height and velocity for efficient data-gathering from SNs [20]. Many path planning methods can be used for UAVs to navigate in a three-dimensional (3D) domain filled with obstacles.  
Path planning can be achieved using classical or heuristic algorithms. Classical algorithms are simple and can be implemented easily; however, they may not efficiently yield complex and dynamic environments. These include cell decomposition, potential field, and sampling-based methods. Probabilistic roadmap (PRM) and rapidly exploring random tree (RRT) schemes belong to classical algorithms. Heuristic algorithms are used for optimization and robustness. They include neural network, fuzzy logic, and nature-inspired algorithms. Genetic algorithms (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and artificial bee colony (ABC) schemes belong to heuristic algorithms. Our study has exploited a hybrid approach involving both classical and heuristic algorithms to achieve better results.