# Sensors and Communication Simulation for Unmanned Traffic Management
## 7 Conclusions and Future Work  
The worldwide drone usage expansion demands the technological development of UTM systems. Due to their complexity and critical nature, it is necessary to evaluate UTM platforms in a wide variety of scenarios to verify its safe operation. Currently, these tests are performed by carrying out real flights that reproduce comprehensive scenarios. However, as we discussed in the introduction, this procedure is limited and not very scalable. The usual solution to solve these limitations is to resort to simulation techniques. As we depicted in Table 2, after analyzing existing literature, we can conclude that there are no complete simulation solutions available. More specifically, current solutions do not consider the impact of communication networks and surveillance sensors which are key for UTM systems functioning. To solve this problem, we proposed a comprehensive agent-based simulation architecture that allows to simulate the behavior and interactions of the main UTM related agents: drones, pilots, ground control stations, surveillance sensors, and communication networks. The final goal of this platform is to provide to a set of simulated signals required by a UTM system in both pre-flight and in-flight stages, such as: flight plans, surveillance  
Figure 19: (**a**) Traffic processed by each CNode separated from its origin. (**b**) Delay experienced by UTM-received packages from flight 2 with three different network configurations. Figure 20: Simulated trajectory for each flight in this scenario along with the expected trajectory, received UTM telemetry and CNode ranges. tracks, telemetry information, alerts, etc. This way, it is possible to perform a system-level evaluation of a UTM platform and analyze possible emergent behaviors. This architecture is based on microservices and provides a series of abstractions and mechanisms that allow modeling the behavior of such agents in a simple, reusable, and extensible way. It also allows exchanging events and messages between agents in order to simulate their interactions. Moreover, the proposed simulator generates a common simulation environment for the simulated agents and the UTM platform including weather, terrain, and airspace information. It also allows manually scheduling contingencies (in addition to those arising naturally during agent's interactions) so that the UTM system can be tested in the presence of unexpected behaviors. Based on this architecture, we have implemented a simulator where we have modeled each one of the actors with a sufficient level of detail to be able to generate the aforementioned input signals. During the validation of the system, we demonstrated that it is possible to use this architecture to simulate complex scenarios in which several agents interact in concurrence of unforeseen situations that are forced by the user. We also verified the correct functioning of the UTM-simulator interconnection mechanisms and those envisioned to provide information to user-defined evaluation services. To conclude, the proposed simulator constitutes a valuable complement and, ultimately, an alternative to the use of real tests for the validation of UTM systems. Its implementation results in a reduction of the development costs of these systems and an improvement in the achieved levels of operational safety. In order to improve the use of the simulator, additional development efforts are planned, some of which have already been introduced. Currently, UTM feedback information is not used during the flight phase as we have designed an open loop system. Clearly, the behavior of the pilots, who ultimately determine the behavior of the drones, is not unrelated to the information received from the UTM system, such as alerts. Therefore, it would be appropriate to extend the presented simulator to take into account the output information produced by the UTM system, thus converting the simulator-UTM platform system into a closed-loop system.