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Ayass et al. Intell Robot 2022;2(1):20-36 https://dx.doi.org/10.20517/ir.2021.07 Page 32

From the criteria established in the architecture, it is noted that at around Second 19 of simulation the
handover is performed, causing the average flow rate to reach twice the value of the previous connection
and remain stable until the end of the simulation. This better performance is possible due to the
management made by the proposal of handover with the fuzzy system, which prevents the UE from
selecting a new access point where the UAV is about to discharge, even if it presents good signal strength.

In the second scenario illustrated in Figure 7, the network was subjected to a greater demand for data due to
the increase in the number of users overloading the network. It is possible to see that, without the proposed
solution, the network presented an even worse performance than in the previous scenario, where the flow
rate drops drastically when using the traditional handover model.

The running application is also CBR type, and, by the traditional model, the transfer was made to the
nearest network, even though there was no interruption in the connection. The new network was more
overloaded and ended up causing the throughput to be below 0.1 Mbps. Conversely, the proposed method
performed the handover only when necessary and maintained a stable connection when selecting a better
network.

As in the first scenario, the handover with intelligent management of parameters by the fuzzy system was
more efficient as it managed to maintain a constant connection, in addition to identifying the best access
point and promoting a better flow rate to the UE from Second 90 of the simulation.

The study also analyzed the effects of the traditional process of handover and the one proposed by fuzzy
inference, through simulations involving video application. To evaluate the quality of the media received,
the QoE results in the same previous scenarios were compared. The video used in the simulation has a
resolution of 176 × 144, 1000 frames, and decoding in YUV 4:2:0 format, which stands for the color
difference encoding system whether composite or component.

In this way, the peak signal-to-noise ratio (PSNR), structural similarity metric (SSIM), and video quality
metric (VQM) metrics were considered, being these objective metrics that complement each other, to assess
the impacts of signal degradation in the original video with the reference when the traditional handover was
performed, as well as the proposed one. At the end of the transmission, the values of the metrics in question
were calculated and displayed frame by frame.

PSNR has a range of values between 0 and 100 dB. For values above 30 dB, it is understood that the video
has good quality. On the other hand, videos that are below the 20 dB range are considered of poor quality.
For the network in the first scenario, Figure 8 shows the PSNR values for each frame of the video.
Comparison with the original file shows that the PSNR of the video received had an average of 21.3 dB in
the traditional handover, classifying it as a low-quality video. Differently, the PSNR for the video with the
Fuzzy criteria obtained an average of 42.41 dB, characterizing it as being of excellent quality.

The range of SSIM values is between 0 and 1, where 1 represents the exact correlation with the original
image. Figure 9 shows the SSIM values of frames in the traditional handover obtaining an average of 0.59.
For the proposed handover, the result obtained was 0.98, being very close to 1, which is the accepted value
for a perfect correlation of images. In this sense, it could be seen that the reference video had low distortion
for this parameter.