## 2 Related Works
[22] discussed the problem of data collection using a WSN with an altitude-controlled UAV and confirmed that when the UAV reached the required height and the speed became constant, the data delivery became steady, and the risk of data loss decreased. Multiple types of UAVs have been developed for various purposes. Parameters employed for UAV classification include the maximum takeoff weight (MTOW), operating conditions (altitude and range), autonomy, and size [23]. In many civil applications, the vertical takeoff and landing of rotary-wing UAVs and UAVs with an MTOW of <25 kg have received considerable attention. Such UAVs are commonly referred to as small drones [24]. Most studies on mobile sensor movement have focused on applying localization algorithms to sensor nodes or on determining the required node locations of mobile beacons or anchor nodes. These mobile beacons or anchor nodes are used for locating a monitored event when it occurs. [PERSON] et al. [25] proposed a spiral movement trajectory of a mobile beacon to enable the beacon to broadcast its location information. [PERSON] et al. [26] partitioned the monitoring area into several grids and introduced a movement algorithm designed for beacon nodes. The unlocated sensor nodes can obtain the received signal strength indicator values when they communicate with beacon nodes and use three-point localization to calculate their locations. [PERSON] et al. [27] presented a modified Hilbert path for beacon nodes. It improved the positioning accuracy of unlocated sensor nodes. [PERSON] et al. [28] proposed a path planning mechanism called ZSCAN for mobile beacon-assisted localization. The beacon node can locate all deployed sensor nodes by moving along a given path. [PERSON] et al. [29], in consideration of the limitation of the maximum movement distance of a beacon node, a node localization algorithm for a WSN with a mobile beacon node (NLA_MB) was developed. The NLA_MB algorithm partitions the movement area of the beacon node into several hexagonal grids and establishes an optimization model of node localization errors according to the movement path constraint, movement distance constraint, and other beacon node-related constraints. [PERSON] et al. [30] introduced two novel dynamic meta-heuristic optimization techniques for achieving mobility-assisted localization in WSNs. Using optimization algorithms, the models determine the path of the mobile anchor, which facilitates localization ratio maximization and localization error minimization. Some recent studies have described the use of natural heuristic artificial intelligence (AI) methods [31; 32; 33; 34; 35; 36; 37; 38] for optimizing WSNs' energy consumption. Examples include using improved particle swarm algorithms and virtual force algorithms to solve the sensor nodes' positioning problem [31]; using a multiobjective GA to optimize the network lifetime for designing energy-efficient WSNs [32]; using the ant-based routing algorithm to maximize energy efficiency [33]; and using GAs to establish a set of ideal parameters (including energy consumption and route lifetime) [34]. Regarding energy cost optimization, sensitivity area, and network reliability, one study made performance comparisons of GAs and swarm intelligence algorithms, namely the bees algorithm and the firefly algorithm [35]. In other studies, an artificial bee colony algorithm was developed for optimizing sensing data collection paths to minimize energy consumption [36], where an energy-efficient, harmony search-based search algorithm was developed [37], and a GA-based optimization approach was employed for rapid, automated energy management [38]. Because the energy of each sensor node is limited, unless the battery life of sensor nodes can be extended, determining how to reduce the energy consumption of WSNs is crucial. The natural heuristic AI method was used to provide a reasonable and rapid solution for optimizing WSN performance [32; 33; 34; 35; 36; 37; 38].