# Handover Management for Drones in Future Mobile Networks--A Survey
## 7 Future Directions
### Energy Efficiency  
As previously mentioned, one of the significant challenges facing drone networks is the limited power, which may lead to the termination of drone operations in certain cases. Limited power is a substantial challenge that may also lead to increasing the frequency of handovers. Connected drones require more power as compared to terrestrial UEs due to their connections and movement characterizations. For example, when drones move in 3D with high speed, the handover probability will increase. This leads to increasing the handover signaling, which in turn leads to more power consumption. Therefore, to reduce the power consumption of drones, more efficient mobility techniques, and energy-efficient techniques must be used. Renewable energy methods, which are also effective solutions, should be considered, especially for those remotely controlled from long distance. This  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Ref** & **Year** & **Study Focus** & **Proposed Method** & **Solution Target** & **Environment** \\ \hline
[132] & 2021 & Mobility in drone taxi applications & Deep reinforcement learning & Compute the optimal transportation routes & UAV mobile network \\ \hline
[133] & 2021 & Dynamic object tracking on UAV system & A learning-based UAV system & Achieving autonomous surveillance & UAV mobile network \\ \hline
[134] & 2022 & Power-Efficient Wireless Coverage of UAVs & Multi-UAV 3D deployment with power-efficient planning & - Reducing the transmit power - Meeting users’ data rate requirements. & UAV mobile networks \\ \hline
[135] & 2022 & Fast Multi-UAV Path Planning for Optimal Area Coverage & Software framework and an algorithm & Obtains optimal UAV paths to Complete the overall mission at the minimum time. & UAV mobile networks \\ \hline
[136] & 2018 & Drone-delivery using autonomous mobility & Drone-delivery using autonomous mobility (DDM) & Solve: (1) high demand of delivery; (2) short delivery lead-time; and (3) complex traffic congestion. & - \\ \hline
[137] & 2020 & Performance characterization of mobility characteristics & Performance analysis & Characterize performance of several canonical mobility models in a drone cellular network & Drone cellular Networks \\ \hline
[138] & 2020 & Mobility and service-oriented modeling & Neuro-fuzzy interference system & Assist in reliable and efficient route selection & Ad hoc networks \\ \hline
[139] & 2021 & Optimization for drone mobility & Q-learning & Optimize handover decision to provide efficient mobility support with high data rate in time-sensitive applications, tactile Internet, and haptics communication & 5G and Beyond Ultra-Dense Networks \\ \hline
[140] & 2020 & Drone mobility support & Reinforcement learning/Q-learning algorithm & Ensure robust wireless connectivity and mobility support for drones in the sky & Long-term Evolution (LTE) and the Fifth-Generation New Radio (5G NR) \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._is a research direction that can be pursued in the future since more effective solutions are required.