# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
## 2 Related Works  
In recent years, UWSN applications have attracted considerable attention from industry and academia. UAV flight path optimization has been considered in several prior studies [28; 29] from different perspectives related to the targeted application. A range of techniques related to mobile robots in WSNs is reviewed in [30]. Additionally, an extensive survey on recently developed methods that exploit the motion of sensor nodes and/or sink(s) to prolong the lifetime of mobile WSNs is presented in [31]. Each study aims to optimize specific parameters, such as the consumed energy, flight duration, travel distance, and task offload efficiency.  
In [32], ACO-based mobile sink path determination for wireless sensor networks was proposed to maximize the network lifetime and minimize the delay in collecting data from the sensor nodes. UAV path planning with safety requirements was primarily investigated. In [33], the collision-free trajectory-planning algorithm based on RRT and optimized RRT (RRT*) was studied for solving detected conflicts for UWSNs.  
A new path planning algorithm based on spiral decomposition, called the spiral path planning algorithm, was proposed for WSNs deployed in remote tactical edge networks that did not have the required infrastructure [34]. The optimal trajectory planning for UAV-assisted data collection in WSNs under the age of data constraints was elaborated in [35]. In [36], a PSO-based optimization method was proposed to obtain waypoints for a UAV to reduce the energy consumption and bit error rate of SNs. The UAV travel time was presented. The cooperative relay was employed for efficient data gathering, and waypoints were selected freely. An algorithm named efficient routing strategy for UAV, based on ACO for UWSNs, was proposed to monitor farmland information in remote mountain areas [37]. In [38], a simulated annealing algorithm was employed to detect the optimal flight trajectory based on the WSN architecture. In [39], deep learning (DL) trained by a GA was presented to improve path planning efficiency for data collection using multiple UAVs. 6 G and deep reinforcement learning (DRL)-based path planning is studied in [40]. Joint genetic algorithm and ant colony optimization for optimal path selection in accordance with sensing, energy, time, and risk utilities are discussed in [41]. Dynamic programming (DP)-based path planning is studied for path planning in UWSNs in [42]. The existing works for path planning in UWSNs are summarized in Table 1.  
From the literature study, we can say that most of the path planning works in the literature are dedicated to finding a collision-free, energy-efficient, and age-optimal path. They all contribute to an effective path planning approach. However, they do not address 3D network scenarios and the different constraints of dynamic environments. Moreover, for emergency environments, a delay-sensitive algorithm is highly desired. Hence, we have attempted to design the shortest and safest path between the target area and the BS in a 3D dynamic environment using the shortest roadmap and the fastest converging optimized ABC algorithm.