# Sensors and Communication Simulation for Unmanned Traffic Management
## 4 Agent-Based Simulation in a Distributed Architecture  
### Simulation Approach  
To model all the behaviors identified in the previous section, we will implement them as a composition of the interactions and states of all the actors using an agent-based simulation approach. As previously stated, due to its inherent down-top approach, agent-based simulation is of special interest for the discovery of emergent behaviors, which in our case is vital to guarantee the safety of UTM systems. Hence, each of the individual actors (drones, GCSs, network node, sensor) will correspond to an agent that will be modeled individually. These agents will autonomously update its state and interact with other agents at simulation time.  
To do so, we propose a mixed closed/open loop, real-time, model-agnostic, and scenario-centered microservices simulation architecture that provides the necessary abstractions and mechanisms to easily model agents and execute simulation scenarios in a simple and reusable way.  
Our current simulation architecture closes the management loop for pre-flight (strategic) operations, enabling testing of automated flight plan authorization by UTM and making simulated flights following amended flights proposed by it. Regarding in-flight control, the simulator is open loop. By open loop, we mean that although agents will take into account the states and interactions of other simulated actors, they do not consider UTM-provided tactical information such as alerts on nearby traffic. This is a current limitation of our simulator, to be addressed in future iterations.  
Regarding time management, we are facing an eminently dynamic simulation that requires that all agents evolve synchronously through simulated time. In this sense, some evaluations (e.g., tracking and monitoring evaluation) could benefit from an accelerated time simulation approach, and also by the implementation of Monte Carlo simulation (repeated execution of experiments for statistical assessment). In the current version of our  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{Detection by non-cooperative sensors} & \multicolumn{2}{c}{} & \\ \cline{2-5} Sensors & Detection by cooperative sensors & & & \\ \cline{2-5}  & Detection errors (precision, false alarms, etc.) & & & \\ \cline{2-5}  & Plot processing to generate tracks & & & \\ \cline{2-5}  & UTM system interaction & & & \\ \cline{2-5}  & Contingencies response & & & \\ \hline \multirow{4}{*}{Network} & Routing and forwarding & & & \\ \cline{2-5}  & Packets disturbances & & & \\ \cline{2-5}  & Mobile communication nodes with range & & & \\ \cline{2-5}  & Compatibility with communications technologies & & & \\ \cline{2-5}  & Contingencies response & & & \\ \hline \multirow{4}{*}{Environment} & Airspace definition & & & \\ \cline{2-5}  & Weather information & & & \\ \cline{2-5}  & Realistic terrain model & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(Cont.\)simulator, we implemented real time simulation instead of an accelerated time simulation due to limitations in the evaluated UTM platform (need to override timed processes with a common time reference). This is another area of future improvement of our proposal.  
The proposed simulator is model-agnostic in the sense that it allows defining and simultaneously using multiple user-defined models for a given agent type. In addition, since we must model dynamic systems with random behaviors, the agents' implementations will include stochastic parameters guaranteeing that the different simulation realizations are reproducible. All simulations are performed by defining reusable scenarios. These scenarios constitute the basic simulation unit, and their definition comprises all the required information to reproduce a simulation exercise, including details related to each agent, the models used to simulate them, the simulation environment, user-defined contingencies, and timing information.