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Ayass et al. Intell Robot 2022;2(1):20-36 https://dx.doi.org/10.20517/ir.2021.07 Page 28

instantly, which advantages a fuzzy approach [8,11] .

Based on the survey of related literature (see Table 1), it is noted that the applicability of traditional
handover schemes, as well as new propositions that support energy consumption and QoE, are still poorly
investigated.

Thus, this paper proposes a fuzzy system strategy for handover decision, that is, combining support for
mobility level, battery, displacement direction, throughput, and received signal strength indication (RSSI),
proving that a fuzzy system is a promising technique for contributing to UAV networks.

5. FANET FUZZY SYSTEM EVALUATION
UAVs comprise a significant part of future wireless communication networks, acting as a mobile base
station. While these devices provide several solutions related to mobile communication networks, UAVs
also have numerous challenges, especially when it comes to handover management. Unlike terrestrial
networks, drones are mobile devices that move in a 3D environment, which further complicates mobility
issues.

Handover is one of the essential processes in wireless communication networks that guarantee continuous
connection and quality of service while users are mobile. The criterion for the conventional handover
decision is based primarily on the RSSI to indicate whether the device will remain attached to the current
point of access or not. In the context of FANETs, this single premise for network selection can result in
failures or even interruptions in service, since the UE can connect to a UAV with a low battery level, which
is one of the most critical factors in these devices.

Similarly, high user mobility can compromise the quality of experience, due to the excessive number of
handovers and the “ping-pong” effect that can direct the UE to a saturated network that offers low
bandwidth.

Given this context, this work contributes with a study case that consists of presenting a system based on
fuzzy logic as it is widely used in dynamic scenarios, as in the case of networks composed of UAVs, to assist
in the decision making of handover in a FANET. The fuzzy system considers three input parameters: user
speed, RSSI, and drones battery level. These inputs are processed by the inference system for the defuzzifier
to evaluate and generate the decision-making outputs.

In fuzzy systems, the results are classified into a range from 0 to 1. A value of 0 denotes an absolute
exclusion, while a value of 1 denotes a complete correlation. The gap between the two extremes results in
intermediate degrees of relevance. Elements can also belong to two or more defined sets, observing the
values of the membership functions for each element.

One or more linguistic variables can be associated with the set of Fuzzy values, which represent the universe
of the possibility of the expected results. In this work, the terms used to classify the outputs with the
possibility of triggering the handover are: no, probably no, probably yes, and yes. The handover process is
executed when the inference value is equal to or greater than 0.6.

The system considers three input parameters that are processed by the inference system so that the
defuzzifier can evaluate and generate the decision-making outputs. The first is related to the user’s level of