# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 2 Flying Ad Hoc Networks  
In this subsection, we briefly discuss FANETs, their components, and the functionalities of each component. We then define the dynamic topology of FANETs. We also discuss the differences of FANETs from other ad hoc networks. Finally, we review the suitable mobility models for FANETs according to the application scenario. FANETs primarily consist of UAVs that mimic the behavior of swarm intelligence and collaborate with each other and the terminal BS/IoT devices/sensors/GUs or edge-fog-cloud to form an autonomous self-organized multi-UAV communication system. In FANETs, terrestrial devices are swapped with UAVs and can establish communication in any type of emergency without requiring any fixed network infrastructure. In emergency applications, when a terrestrial communication infrastructure or ground sensor network is unavailable, UAVs can be deployed to sense remote areas, utilizing their advantages, such as flexible 3D mobility, fast deployment, and large birds-eye vision. Each UAV in the swarm can sense, execute a computationally intensive task locally or by offloading to the nearest edge server, communicate, cache data, and operate as a router to forward remote \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Protocol Type**} & \multicolumn{8}{c}{**Important Features**} \\ \cline{2-9}  & **Link Quality** & **Routing** & **Routing** & **Holes** & **Energy Holes** & **Delay** & **Blind Path** \\  & **Assessment** & **Avoidance** & **Avoidance** & **Avoidance** & **Optimization** & **Avoidance** \\ \hline Position-based & & & & & & & \\ forwarding & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ techniques & & & & & & & \\ \hline QL-based position & & & & & & & \\ aware routing & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ protocols & & & & & & & \\ \hline \hline \end{tabular} Note: “\(\times\)”: The corresponding feature is not supported; “\(\surd\)”: The corresponding feature is supported.  
\end{table} 
Table 4: Important features of position-based forwarding techniques and QL-based position-aware routing protocols. UAV sensing data to the BS for further processing. Thus, a FANET has two major parts: terrestrial and non-terrestrial parts (Figure 1). The terrestrial section frequently consists of single or multiple fixed or mobile BSs, charging stations, edge-computing servers, GUs, and different types of sensors or IoT devices. The mobile BS can be a ground vehicle with charging stations at the top of the vehicles or edge-computing servers to perform computationally intensive tasks for UAVs. FANETs can also collaborate with existing terrestrial BSs on an on-demand basis to expand the capacity of wireless networks. When the residual energy level of an UAV reaches the minimum threshold level, UAVs can leave the aerial network through an optimal charging-scheduling algorithm to obtain energy replenishment at particular wireless charging stations located at the BS. Instead of wireless charging techniques, UAV batteries can be swapped at the BS before returning to the aerial network. The BS can also function as a gateway to connect FANETs with external wired networks, i.e., the Internet. The non-terrestrial section consists of a set of homogenous or non-homogenous [1] UAVs operating collaboratively at different altitudes. There are many types of UAVs, and researchers select the UAV type according to their application requirements. In [26; 30], the authors classified UAVs according to their size, flying altitude, payload capacity, battery capacity, and endurance time. They classified UAVs into high-altitude platforms (HAPs) and low-altitude platforms (LAPs). HAPs have a high payload capacity, long endurance, and high-energy storage systems. HAPs are quasistatic and are deployed to perform a long-term mission. In this survey, we primarily focus on LAP UAV missions.