# Position-Monitoring-Based Hybrid Routing Protocol for 3D UAV-Based Networks
## 3 Methods
### 3D Free Space Model for PMHRP  
Generally, UAVs operate in an outdoor environment; therefore, a 3D free-space propagation model is considered, where each UAV transmits and receives data information without any hurdles [42], which creates an actual 3D environment and free-space propagation. For a stable communication link, it is necessary that the neighboring UAVs are in communication range of each other; therefore, transmitting power and receiving power are calculated using Equation (1):  
\[\frac{Pr_{UAV}}{Pt_{UAV}}=\frac{At_{UAV}*Ar_{UAV}}{d^{2}\lambda^{2}} \tag{1}\]  
where \(Pr_{UAV}\) is the receiving antenna signal power, \(Pt_{UAV}\) is the transmitting antenna signal power, and \(At_{UAV}\) and \(Ar_{UAV}\) are the effective areas of the antenna at both sides. \(\lambda\) is the wavelength of the signal, and d is the distance between the receiving UAV and transmitting UAV antennas, accordingly. An isotropic antenna is adopted in a 3D free-space  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**References** & **Routing Type** & **Limitations** \\ \hline
[38] & Topology based & High overhead, link risk, and high normalized load \\
[20] & Topology based & High overhead, link breakage, and loop routing \\
[39] & Topology based & High overhead, higher bandwidth \\
[10] & Topology based & High delay and higher risk of link breakage \\
[18] & Topology based & High overhead, high bandwidth consumption \\
[31] & Geographic based & High energy, high normalized load, and loop routing \\
[24] & Geographic based & High normalized load, high delay, and high energy consumption \\
[40] & Geographic based & Loop routing, high energy, and flooding route \\
[32] & Geographic based & Loop routing, high energy consumption \\
[41] & Geographic based & Loop routing, high energy, and flooding route \\  & & Unsuitable for ultra-dense networks, PHC vulnerable to attackers, and limited \\  & & routing between UAVs if all PHCs are dead. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Limitations of Existing Work.  
model; therefore, an effective area of the antenna is \(\lambda/4\pi\), and adding in Equation (1) gives us Equation (2):  
\[\frac{Pr_{UAV}}{Pt_{UAV}}=\frac{\lambda^{2}}{\left(4\pi d\right)^{2}} \tag{2}\]  
Generally, the gain at both sides (transmitting and receiving) is not equal to 1; therefore, it is necessary to consider the gain at the receiving and transmitting antenna using Equation (3):  
\[\frac{Pr_{UAV}}{Pt_{UAV}}=Gt_{UAV}~{}Gt_{UAV}\frac{\lambda^{2}}{\left(4\pi d \right)^{2}} \tag{3}\]  
Here, \(Gt_{UAV}\), \(Gt_{UAV}\) are the gain at the transmitting and receiving antenna, respectively. Several UAVs move across the wireless network to form a 3D environmental network by taking into account three coordinate mechanisms. Suppose there is a set of \(N=\left\{u_{1},u_{2},\ldots~{}u_{n}\right\}\), where \(u_{i}\) represents the UAV. The proposed work utilizes the 3D coordinates to find the position of each UAV by utilizing the Euclidian distance (Equation (4)):  
\[D_{n}=\sqrt{\left(x_{j}-x_{i}\right)^{2}+\left(y_{j}-y_{i}\right)^{2}+\left(z_ {j}-z_{i}\right)^{2}} \tag{4}\]  
In Equation (4), distance \(D_{n}\) is the distance of each UAV, and it can be calculated by subtracting the three coordinates \(\left(x_{j},y_{j},z_{j}\right)\) with respect to the coordinates of the initial reference point \(\left(x_{i},y_{i},z_{i}\right)\). After the formation of the 3D free-space model, the next step is to generate a mobility scenario for UAV movement. We have adopted the random waypoint mobility model to create a 3D network environment. The main reason behind choosing the random waypoint mobility model is that it supports 3D environment and offers a ad-hoc network environment where UAVs can select the next position independently.