# Enabling Multi-Mission Interoperable UAS Using Data-Centric Communications
## 5 Functional Validation
Analogously, Figure 4d represents the data traffic corresponding to the video camera of the tactical UAV. For simplicity, and similar to the previous case, the figure only shows the data received from one of the RPIs. Our practical results confirm that DDS can effectively support the flexible deployment of applications and services, and eases the sharing of information and situational awareness in UAS deployments, through the utilization of application-defined topics and the automated discovery of the published information, which can be requested with specific QoS requirements. As we have commented, a straightforward implication of our virtual layer-2 network configuration is that all the communicating endpoints will be sharing the same diffusion domain, so the advantages provided by multicast delivery can easily be acquired without the need of further multicast specific infrastructure (e.g., the execution of a multicast routing protocol at the RPIs). As a disadvantage of our VXLAN-based configuration, we can mention that all multicast/broadcast control and data traffic is in fact received (and eventually discarded) by all the terminals in the broadcast domain. Although this issue could be addressed with the integration of well-known techniques to learn on active multicast subscriptions on layer-2 network segments [32], it has however been considered to present an assumable cost in this type of critical scenarios, where such redundancy may be beneficial for instance in terms of better handover performance (i.e., when a mobile station changes its Wi-Fi access point). To verify this, we carried out a second experiment, where the user working at the distant area moves and changes its Wi-Fi access point to the network, obtaining network access connectivity via the same small-size UAV that serves the mission planner. Figure 5 represents the throughput of data traffic perceived by the user in this experiment, where we can see an interruption on the reception of the real-time video stream. This interruption corresponds to the time required to execute the attachment procedures to the new wireless access point. Once these procedures are completed, the user continues receiving the video content, as this content is also being served through the new access point. Finally, we used the deployed infrastructure to perform an experiment aiming at evaluating the effective throughput that can be obtained using DDS. For this purpose, we used the RTI Perftest tool to estimate the throughput that can be provided by the DDS middleware to applications, considering application-level data units (that is, the number of data that are published at a time by a DDS application) of two different sizes: 1000 bytes and 64 bytes. For each of these sizes, we used the  
Figure 4: DDS signaling (**a**,**b**); and data exchange (**c**,**d**). Figure 5: Interruption of the reception of real-time video due to handover. RTI Perftest tool at the tactical UAV to publish data at different rates (1 Mb/s, 2 Mb/s, 4 Mb/s, 8 Mb/s and 32 Mb/s), with another instance of the tool subscribing to that information from one of the client devices. For each rate and size of the application-level data unit, we measured the throughput received at the subscriber. To better frame the efficiency achieved by DDS, we repeated the same experiment using the Iperf tool ([[https://iperf.fr](https://iperf.fr)]([https://iperf.fr](https://iperf.fr))), which can be used to send data traffic over UDP at the aforementioned rates, using the same size of the application-level data unit. The results of these experiments are shown in Figure 6. In this figure, we observe that, for a large size of the application-level data unit, the performance of DDS closely follows that of UDP; on the contrary, with a low size of the application-level data units, the performance of DDS decreases, due to the data overhead introduced by the DDS middleware.