# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 2 Related Works
Interestingly, although most UAV communication research focuses on deploying UAVs as aerial base stations (i.e., cellular technology such as 3G/4G/5G), most testbeds use WiFi (IEEE 802.11 b/g/a) as communication protocol such as in this work. Only in [19] one can find a literature review with particular emphasis on the energy-efficiency perspective. Besides pointing to +20 other UAV communication-related existing surveys, the authors discuss the main factors affecting UAVs' energy consumption. One of these factors is UAV placement. As such, the authors review energy-efficiency algorithms for optimal UAV placement in the literature. [42] suggests using Machine Learning for the optimal placement of UAV-mounted base stations given a predicted traffic demand. In their approach, they first find the optimal partitions of service areas. Then, the optimal location of each UAV that minimizes the total power consumption is derived. While the authors model the transmit power of UAVs deployed, they fail to use a mechanical energy model and simply minimize the UAVs travel distance, assuming that the mechanical power is proportional to it. [43] proposes a solution to minimize UAV's mechanical and communication energies while integrating cognitive radio technology. The optimization problem is solved using a deterministic algorithm based on the Weber formulation and its performance is compared to that of a meta-heuristic algorithm, namely particle swarm optimization algorithm (PSO). In this case, the authors model the total energy consumption of a drone with two components, the hover and transmission energies. Finally, Table 1 compares the main related works with our proposal. This work's uniqueness lies in a new objective function for the problem of multi-UAV optimal 3D placement. The consideration of WiFi and its mac-sublayer on the speech quality (as a constraint) can only be found in [12]. However, [12] does not model the energy consumption in the UAVs and has a different objective function, as so does other works that model the mechanical and communication power in UAV-installed base stations such as [42; 43]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Paper** & **Objective** & **Multi-UAV** & **UAV+to-Ground** & **UAV** & **UAV** & **User** & **Speech Quality** \\  & & & & **Technology** & **Mechanical Power** & **Radio Power** & **Radio Power** & **Constraint** \\ \hline
[26] & minimize transmit power & no & cellular & no & yes & no & no \\
[25] & maximize coverage with minimum transmit power & no & cellular & no & yes & no & no \\
[91] & maximize capacity and extend battery life of mobile users & yes & cellular & no & yes & yes & no \\
[32] & minimize transmit power of mobile users (deployment and mobility) & yes & cellular & no & yes & no \\
[22] & minimize number of UAVs & yes & cellular & no & no & no & no \\
[39] & minimize the total transmit power of the entire aerial system & yes & cellular & no & yes & no & no \\
[42] & minimize total power consumption given predicted traffic & yes & cellular & yes & yes & no & no \\
[43] & minimize total power consumption & yes & cognitive radio & yes & yes & no & no \\
[12] & minimize number of UAVs & yes & WiFi & no & yes & yes \\ this paper & minimize the ratio between the number of UAVs and UAVs’ endurance & yes & WiFi & yes & yes & no & yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of main works related to 3D optimal placement of UAVs for communication.