# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 8 Conclusions and Future Research  
This paper formulates a new problem for the UAVs' optimal 3D placement to offer VoWiFi services with guaranteed speech quality to ground users confined in a known area. Our objective function minimizes the ratio between the number of UAVs initially deployed and their average endurance expectation, providing the layout that requires fewer drones per hour of service. Solutions are found using two well-known search methods: (a) a genetic algorithm is proposed for the initial deployment, and (b) a particle swarm optimization algorithm is proposed for updating UAVs' location in scenarios with users mobility. A numerical analysis compares the results of our optimal placement algorithm for unicast and broadcast VoWiFi services. Our results for the unicast service show that the drone turnover rate is related to both, users' sparsity and the number of associated users to each drone. In terms of energy consumption, radio communications represent between a small part (between 14% and 16%) of the total power consumption depending on the density of users served per drone. Large areas tend to locate drones at higher altitudes, which increases UAVs' mechanical energy consumption. The broadcast VoWiFi service generates a very low traffic load, which translates into a significant reduction of UAVs deployed and total power consumption (between 20% and 75%), and less sensitivity to the number of served users. The next step in our work is to consider two new aspects in this field: (a) interferences between APs, so that overlapping channels could negatively impact on QoS; and (b) using 5G instead of WiFi as the enabling technology for the UAV-to-Ground access network. Conceptualization, V.M. and R.E. ; methodology, V.M. ; software, V.M. ; validation, V.M., R.E. and A.E. ; formal analysis, V.M. and R.E. ; investigation, V.M. and R.E. ; resources, G.M. ; data curation, V.M. ; writing--original draft preparation, A.E. and V.M. ; writing--review and editing, A.E. and V.M. ; visualization, V.M. ; supervision, R.E. ; project administration, R.E. ; funding acquisition, R.E.. All authors have read and agreed to the published version of the manuscript. This work is partially funded by the research grant number 2020/00000172 from the Regional Government of Andalusia (European FEDER 2014-2020 Andalusia Tech program). The authors declare no conflict of interest. The expected energy consumption per slot can be computed by averaging each consumption event with its probability of occurrence as proposed in Equation (32). Each state on the WiFi NIC (transmission, reception or idle) presents a different power consumption termed as \(\rho_{\text{tx}},\rho_{\text{rx}}\) and \(\rho_{\text{idle}}\)  
Figure 9: Comparison against other approaches. respectively. In addition, some frame exchanges may last longer that others depending on their result (success, collision or corruption). Consequently, the following events can be identified:  
The absence of transmission attempts results into an idle channel event, whose consumption (in Joules) is represented by \(J_{\sigma}^{(j)}\) for the _j-th_ station (notice that consumptions may differ between stations and access points) and its probability of occurrence \(P_{i}\) is common to every station in the network:  
\[P_{i}=\prod_{j=1}^{S}\left(1-\tau^{(j)}\right) \tag{11}\] \[J_{\sigma}^{(j)}=\rho_{\text{idle}}^{(j)}\cdot\sigma \tag{12}\]  
A successful packet transmission event happens when a single station attempts to transmit while the rest remain silent.