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Page 29 Ayass et al. Intell Robot 2022;2(1):20-36 https://dx.doi.org/10.20517/ir.2021.07

Table 1. Related works
Ping-pong handover Energy efficiency Mobility management QoE
Paper Proposed solution
reduction support support support
[10] Deep learning No No Yes No
[11] Fuzzy No No Yes No
[12] Q-learning algorithm No No Yes No
[13] Coverage decision algorithm which controls No No Yes No
the coverage of each net-drone
[14] Route-aware handover algorithm Yes No Yes No
[15] Dynamic parameters to handover decision No No Yes No
[16] Cooperative game theory Yes No Yes No
[17] Machine learning-based solution No No Yes No
[18] Machine learning-based solution No No Yes No
[5] Q-learning based Yes No Yes No
[19] Fuzzy system Yes No Yes No
[20] Reinforcement learning No Yes Yes No

QoE: Quality of experience.

mobility and indicates how long a mobile device remains in the coverage area of a station. The faster the
device travels, the less time it will be connected to that access point. This first input is divided into three sets
of linguistic values: slow (range 0-1.5 m/s), moderate (1.3-3 m/s), and fast (2.5-4 m/s).

The second input refers to the received signal level, represented by RSSI. This is a factor used to assess how
likely the device is to disconnect from the access point if the signal strength is weak. In this metric, signal
levels are defined for language sets as follows: weak (-120 to -100 dBi), moderate (-115 to -65 dBi), and
strong (> -72 dBi).

The last input metric considers the drone’s flight range, which is linked to how long the devices can remain
in operation. This is an important criterion because, given the knowledge of the remaining time each UAV
can still operate, unnecessary transfers are avoided for those drones that are in the unloading phase and will
not be able to continue the service. For this parameter, the defined sets are: low (0-10 min), medium
(8-20 min), and high (18-30 min) battery levels.

Given the inputs, the fuzzy inference system will determine the outputs according to the set of 27 rules
previously established from the combination of the three parameters. In this work, the Gaussian
membership function is applied to all inputs and outputs. This function is chosen because of its
characteristic of reducing the noise of input variables and its ability to represent real-world phenomena
more naturally.

The output of the fuzzy system indicates the probability of the mobile device starting the handover process.
In general, if a user has high mobility and high levels of RSSI, the transfer process to another network will
not occur. The system indicates a trend of execution of the handover, as its inference value is equal to 0.6.

In the 3D surface graphics in Figure 4, it is possible to visualize the relationship between the chosen
parameters. The region in blue corresponds to a user with high mobility and excellent signal strength. In
this context, the handover process will not trigger. The yellow region indicates the opposite, the user with
low speed and receiving a bad signal; in this case, the handover is executed.