## 2 Related Works
### Drone Path Planning Optimization  
One of the major limiting factors in designing a drone-based data collection system is its battery capacity (and consequently its flight time limitations). Based on the latest battery technology development, the utilization of lithium-ion batteries is considered the best option. Although the capacity of lithium-ion batteries is much larger than that of conventional batteries, the flight time of small drones is still limited to about 20-30 min [47]. Furthermore, increasing the battery size increases the overall weight of the drone. Hence, the problem of power source limitation is considered an unsolved problem for the use of drones in practice. A practical solution to overcome the challenge and enhance the efficiency of drone flight time is the optimization of drone path planning. The authors of [48] studied the impact of drone speed on the performance of the data collection system. The study showed that the optimal drone speed depends on the distance between the UAV and sensor nodes, nodes' transmission power, and amount of data. In addition, dynamic programming was used to optimize the drone speed and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Description** & LoRa & SigFox & NB-IoT \\ \hline \multirow{2}{*}{**Coverage**} & Urban: 5 km & Urban: 10 km & Urban: 1 km \\  & Rural: 20 km & Rural: 40 km & Rural: 10 km \\ \hline \multirow{2}{*}{**Frequency Band**} & Unlicensed & Unlicensed & Licensed LTE Bands \\ \hline
**Maximum Data Rate** & 50 kbps & 100 bps & 200 kbps \\ \hline
**Battery Life** & 20 years & 10 years & 10 years \\ \hline
**Standardization** & LoRa Alliance & SigFox and ETSI & 3 GPP \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of LPWAN wireless technologies. sensors' transmission power, where the objective was to minimize the total flight time over a mission. Another factor that affects drone-based data collection performance is flight altitude. The authors of [49] optimized the drone's flight altitude by minimizing the flight time and maximizing the number of successfully decoded bits in the uplink. Drone path planning is considered a complicated optimization problem, in which the target is to find a superior flight route in an environment under different kinds of constraints. Over the past decade, a couple of methods have been proposed to solve the path planning problem for drones. Generally, path planning methods can be classified into two groups: global (i.e., offline) and local (i.e., online) methods [55]. The global path planning methods are used to find a global optimal path in a known environment, and the generated path is considered as an expected trajectory to be followed by the drone during its mission. On the other hand, the local path planning methods are used for the cases in which the considered environment is fully unknown or partially known. In this case, drones should be equipped with onboard sensors and advanced control methods for real-time environment detection and path planning. Recently, the works in [56; 57] utilized deep reinforcement learning techniques, such as Q-learning, as a promising solution to solve the problem of real-time drone path planning in unknown dynamic environments. Alternatively, heuristics intelligent optimization algorithms have also been widely used in recent years to solve the local path planning optimization problems, such as graph-based algorithms [58], heuristic search algorithms [59], field-based algorithms [60], and intelligent optimization algorithms [61].