# Designing a Softwarized Network Deployed on a Fleet of Drones for Rural Zone Monitoring
## 5 Numerical Results  
In this section, we consider a case study constituted by a fleet of forty drones covering a wide rural area, and working as nodes of the backbone mesh network, all equipped by an SDN/NFV infrastructure to host virtual functions for the video monitoring service. As said in Section 2.1,protocols and techniques applied to setup and maintain connectivity are out of the scope of this paper, and therefore we are limited to assuming that the FANET supporting the backbone mesh network guarantees connectivity with the required throughput between each pair of adjacent nodes. Each backbone node drone carries a package of five Intel NUC Mini PC with processor INTEL i5-3330 @ 3.00 GHz, 64 bits. The monitoring network is constituted by 50 fixed wireless IP cameras installed in strategic points on the territory, and powered with solar energy, and a number of video transmitter drones flying on the same area to integrate video covering of the considered rural zone; this last number has been varied in our numerical analysis in the interval between 0 and 150, in order to have a total number of video transmitters ranging between 50 and 200. Some additional nodes complete the network, one working as backbone network orchestrator drone, four as storage drones, and two installed on the ground to work as fixed storage servers. We have realized two different analyses. In the first analysis, we have varied the number of video transmitters as indicated so far, i.e., in the range [50; 200], while, in the second analysis, we considered a constant number of video transmitters, set to 200, and we varied the number of acquirer nodes active in the network. In our study we assumed that one-third of the video transmitters encode video at 400 kbit/s, one-third at 500 kbit/s and one-third at 600 kbit/s. The number of video receivers for each video transmitter has been randomly chosen in the set [1; 5]. Moreover, 10% of video flows are requested to be recorded. Video receivers can set the destination quality with \(Q_{L}=6\) different levels, the lowest corresponding to a bit rate of 50 kbit/s, and the others with an increment of 50 kbit/s, so ranging between 50 kbit/s and 300 kbit/s. We assumed that 30% of the clients are able to receive video at the maximum quality, while the remaining ones require a quality levels randomly chosen among the six available ones. In the first analysis, we have considered the presence of 5 acquirers and 10 edge streamers, whose position in the backbone network is decided by the network orchestrator to minimize their average distance from the video transmitters and the video receivers, respectively. Obtained results are averaged on 200 simulation runs, each characterized by a different position of the video transmitter drones, chosen in a random way. Parameters characterizing the computational load of each virtual function have been derived experimentally by running them on virtual machines created with 1 core, a RAM of 1024 MB, and a storage space of 5 GB, and using an Ubuntu operating system with Java and gcc. Each virtual machine in idle state uses 0.1% of the processor. The measured parameters are listed in Table 1.  
In order to evaluate the contribution of each virtual function to the overall computational load, in Figure 6 we show their computational loads averaged over all the drones hosting them. As expected, they increase in both the acquirers (see Figure 6a) and the edge streamers (see Figure 6b) with the