## 4 Experiment Design
### Simulation  
The NS simulator, an open source and free application, was used in the experiment. This is a very popular network-level tool for academic work. It can be found at: [[https://ns3-code.com/ns3-download/](https://ns3-code.com/ns3-download/)]([https://ns3-code.com/ns3-download/](https://ns3-code.com/ns3-download/)) (accessed on 21 March 2022). As mentioned above, our proposed models are based on instrumental QoE models, which use three QoS parameters related to the measurement of QoE. QoE is in turn measured using an objective method, VQM [33].  
We used end-to-end testing, featuring a VLC video server (source node) and VLC client (destination node [34]) and a scenario made up using drone swarms. It includes elementsrelated to the swarm characteristics, control unit devices and managing the information and data obtained on each of the missions (See Figure 1).  
If we consider the swarm element, the swarm drones (five drones) have the following characteristics: they are dust resistant and waterproof; they have a longer battery life for longer flights; each drone weighs 250 gr and has an HD camera system with a resolution of 1080 \(\times\) 720 pixels and 180-degree sensor coverage; they can reach a maximum height of 80 m and a maximum speed of 20 m/s; and they have 60 min of absolute flight autonomy and all around-terrain coverage of 40 m. As for the control unit, each swarm has been designed to fulfil a specific flight mission (Environment), which has to be previously programed directly from the control unit.  
Each control unit contains algorithms inspired by pre-existing nature swarms, (known as bio-inspired), leading to correct mission development and to improve the collective intelligence of each swarm. In terms of nature-based algorithms, we used both genetic and particle optimization-types. The type of mission examined in this paper focuses on the identification natural disasters (fires, landslides, floods etc.) in forest and jungle areas; access to hard-to-reach zones and the monitoring of identified areas.  
In terms of managing the data obtained, this mission makes it possible to establish a medium level of scalability, the usual mash-up between the cloud and the distributed architecture. On the other hand, this swarm has a loading and unloading station that makes it possible to increase its general level of autonomy to over an hour [35]. We selected this configuration in order to provide end-to-end quality of service in flying ad hoc networks (VLC video server to VLC client)--in our case, source to destination [36].  
To select the videos, we considered the following measures; these measures are important to provide quality of experience (QoE):  
1. Spatial Information (SI) is a measure of the amount of spatial detail in the image. This is generally higher for complex scenes, i.e., scenes with more objects or borders in a frame sequence yield higher SI values [37].
2. Temporal Information (TI) is a measure of the amount of temporal changes in a video sequence. In general, TI is higher for high movement sequences [38].  
We defined the following ranges for using each model with its respective type of video: (a) Low motion--SI: [30; 40], TI: [5; 10]; (b) Medium motion--SI: [60; 70], TI: [5; 10]; and (c) High Motion--SI: [50; 60], TI: [15; 2]. These were empirically obtained from a systematic review of the 14 databases.  
Table 2 presents the VQM-to-MOS conversion table obtained for our case. The values for each rank depend on the spatial-temporal characteristics of the video.  
Figure 1: Flying ad hoc networks and an environmental natural disaster.  
We define the videos in order to provide QoE as follows:  
The first video (C00) features people, objects or people (not moving or static, Figure 2a) at the top). The second video (I00) features limited movement (Figure 2b) in the middle). The third video (B00) features people, objects or things in motion (Figure 2c at the bottom).