## 2 Related Works
### Drone Path Planning Optimization
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Methodology** & **Result and Findings** \\ \hline \multirow{4}{*}{[50]} & \multicolumn{2}{c}{Experimental:} \\  & LoRa gateway located on 5 th floor of a building & \multirow{2}{*}{SF7 is more vulnerable to the Doppler effect than SF12} \\  & LoRa node on a car roof & \\  & Max speed (km/h): & \\  & urban = 53, suburban = 57, rural = 70 & \\ \hline \multirow{4}{*}{[51]} & \multicolumn{2}{c}{Experimental:} \\  & LoRa gateway on moving car roof & \multirow{2}{*}{Above 96\% packet received at} \\  & LoRa node on a stationary car roof & \\  & Max speed (km/h): line of sight = 120 & \\ \hline \multirow{4}{*}{[52]} & \multicolumn{2}{c}{Experimental:} & LoRa node mounted on Lathe: \\  & LoRa gateway mounted on a 24 m height tower & \multirow{2}{*}{received is 36\% at SF12} \\  & LoRa node on a car roof & \\  & LoRa node mounted on Lathe & \\  & received is 28\% at SF12 \\ \hline \multirow{4}{*}{[53]} & \multicolumn{2}{c}{Experimental:} & LoRa node held by the author: \\  & LoRa gateway installed on a tripod with a height & 80\% packet received at \\  & of 1.5 m & 5 km/h speed \\  & LoRa node held by author & \\  & LoRa node on a car roof & \\  & LoRa node on a car roof & packet received at 24 km/h \\ \hline \multirow{4}{*}{[54]} & \multicolumn{2}{c}{Experimental:} & Above 85\% packet received at \\  & LoRa gateway on top of 3-storey building & \multirow{2}{*}{speeds of 50 km/h, 60 km/h, 70 km/h and 80 km/h} \\  & LoRa node on a car roof & \\  & & with SF12 \\ \hline \multirow{4}{*}{[46]} & \multicolumn{2}{c}{Simulation:} & The drone speed does not \\  & LoRa node on a drone & \multirow{2}{*}{affect the quality of experience} \\  & State LoRa gateway & (QoE), which is the delay, \\  & Speed: 10, 25, and 50 km/h & jitter, packet loss, and output. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Previous research works focusing on LoRa performance vs. Doppler shift. However, the computational complexity is one of the challenges in optimizing the drone path planning, which directly depends on the complexity of the environment profile and problem constraints such as kinematics and dynamics constraints. Fortunately, in large-scale remote farm scenarios, where the sensor nodes are arranged in advance and there are no high obstacles or a dynamic environment, the problem of UAV path planning can be solved based on global techniques, which consequently results in less complex problems which can be solved by metaheuristic intelligent optimization algorithms, such as the genetic algorithm (GA), the ant colony algorithm, and the PSO algorithm. The authors in [60] focused on global path planning under a static environment and used the GA to optimize the drone trajectory under maneuverability constraints. The work in [62] used the wolf pack algorithm to find an optimal path for rotor-wing drones by considering multi-objective cost functions in real and fake 3D spaces. To improve the performance of the wolf pack algorithm, the crossover and mutation operators of the GA were applied to the algorithm. To further reduce the complexity of selecting the optimal path, the path planning problem can be converted to the TSP [63]. The optimization methods for solving the TSP include metaheuristic algorithms and fuzzy neural networks. The authors in [64] modeled the path planning problem for drone-aided data collection as the TSP and proposed a fast path planning with rules algorithm based on grid division to minimize the path distance, where the paths in the divisions are combined based on the line precedence principle. Despite the popularity and wide usage of intelligent optimization for path planning problems, the algorithms should overcome some destructive phenomena such as local optimum trapping and early convergence. In this regard, PSO is an optimal algorithm to solve the TSP due to its powerful ability for local and global search (i.e., exploration and exploitation), which can be considered an efficient algorithm for drone path planning in a large-scale WSN.