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Page 27 Ayass et al. Intell Robot 2022;2(1):20-36 https://dx.doi.org/10.20517/ir.2021.07

[16]
Goudarzi et al. stated that the main challenges of communications assisted by UAVs today are to have
adequate accessibility in wireless networks through mobile devices with an acceptable quality of service
based on user preferences. To this end, they presented a new method based on game theory to select the
best UAV during the HO process and optimize the transfer between UAVs, decreasing end-to-end delay,
transfer latency, and signaling overhead. The results demonstrate the effectiveness of the proposed approach
in terms of handover numbers, cost, and delay.

Azari et al. recommended a machine learning-based approach for the HO mechanism and resource
[17]
management for cellular-connected drones. They offered a machine learning-based solution that captures
the correlations in temporal and spatial levels to help make HO decisions. Peng et al. proposed a solution
[18]
based on machine learning for predicting node mobility. They used the classification of movements to
different classes based on predicting the nodes near a future location.

In the work of Angjo et al. , the handover decision is optimized gradually using Q-learning to provide
[5]
efficient mobility and ping-pong support. The proposed scheme reduces the total number of handovers.
Simulation results demonstrate that the proposed algorithm can effectively minimize the handover cost in a
learning environment.

To avoid the ping-pong handover, Shakhatreh et al. proposed a weighted fuzzy self-optimization (WFSO)
[19]
approach for the optimization of the handover control parameters. The HO decision relies on three
considered attributes: signal-to-interference-plus-noise ratio, the traffic load of serving and target base
station, and user equipment’s velocity. The results indicate that the proposed WFSO approach significantly
lowers the rates of HOPP, radio link failure, and HOF in comparison with the other algorithms found in the
literature.

In this way, several studies have been conducted to address various types of HO issues, mainly in support of
mobility management to reduce handover failures as well as to reduce the ping-pong effect. The ping-pong
effect is the frequent connections and disconnections with the BS as the served device changes locations.

However, few proposals support energy efficiency. Battery capacity is one of the main limitations, becoming
a critical factor for the continuity of the application. Therefore, effective power management is required for
devices that operate on battery power. Some solutions such as wireless charging, solar charging technology,
and even artificial intelligence techniques are indicated for effective energy management that provides
[9]
longer missions.

Finally, Singh et al. proposed reinforcement learning (RL) based on an energy‐aware ABS deployment
[20]
algorithm. Dynamic movements of ABSs are managed by defining the user mobility pattern. However, this
study does not support the quality of experience.

In this way, another critical factor would be the quality of user experience because it can measure the degree
of quality of service through the user’s perception. It is noteworthy that expectations about the satisfaction
of different services and applications vary among different users. This means that QoE is an important
attribute to be considered in the handover decision-making process.

Furthermore, research work carried out in recent years has focused on the field of artificial intelligence.
Approaches based on machine learning and deep learning can ensure improvements in handover decision
making and save computational costs . On the other hand, handover decisions consider several parameters
[8]