# Designing UAV Swarm Experiments: A Simulator Selection and Experiment Design Process
## 3 Generalized Swarm Process Consideration during Development
### Network Topologies and Communication  
Network topologies and communication play a crucial role in the coordination and effectiveness of UAV swarm systems. The selection and design of appropriate network topologies significantly impact the swarm's ability to exchange information, make collective decisions, and execute coordinated actions. Various topologies, such as centralized, decen \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Year Published** & **Study Description** & **Platform Used 1** \\ \hline
[42] & 2022 & A distributed UAV swarm formation system optimized by a hybrid evolutionary algorithm. & ARGoS \\ \hline
[43] & 2022 & Approach to optimized UAV swarm for improved intruder detection. & ArGoS \\ \hline
[44] & 2022 & A framework for simulating cooperative UAV swarms performing joint missions. & OMNET++ \\ \hline
[45] & 2022 & Task decomposition and task correlation of UAV swarm & OMNET++ \\ \hline
[46] & 2022 & Multiple UAVs collaborating for improved field phenotyping. & Gazebo \\ \hline
[47] & 2022 & Resource balancing for MAV (Mobile Aerial Vehicle). & Gazebo \\ \hline
[48] & 2022 & Development of a PSO-based threat avoidance and reconnaissance FANET. & Gazebo \\ \hline
[49] & 2022 & Entrap multiple targets using a robot swarm. & MATLAB + CoppeliaSim \\ \hline
[50] & 2023 & A decentralized method for multiple UAVs to explore separate areas. & Gazebo \\ \hline
[51] & 2023 & A novel IDS (Intrusion Detection System) that identifies deviation in normal UAV behaviors as means of indicating external threats. & Gazebo \\ \hline
[52] & 2023 & A multiple tracking methodology for aircraft at low altitudes. & MATLAB \\ \hline
[53] & 2023 & Collision avoidance strategy for D2D (Device to Device) communications in UAV networks. & MATLAB \\ \hline
[54] & 2023 & Evaluation of routing protocols in UAV ad hoc networks in SAR scenarios & MATLAB \\ \hline
[1] & 2023 & Autonomous cooperative mission planning for multiple UAVs conducting surveillance missions. & MATLAB \\ \hline
[55] & 2023 & Trajectory planning and formation maintenance in swarm using MPC and standoff algorithm. & MATLAB \\ \hline
[56] & 2023 & An improved PSO for optimized base station placement for UAVs. & CoppeliaSim \\ \hline \hline \end{tabular}  
* With regards to the use of MATLAB for experimental design, all cited references may not necessarily use the UAV toolbox, Simulink, or other add-ons, choosing to rely only on the main program. However, they are included in the table since the standalone MATLAB platform may be sufficient for their validation requirements.  
\end{table} 
Table 3: _Cont._tralized, and hybrid architectures [57], can be employed based on the specific requirements and constraints of the swarm application. Centralized topologies, with a central controller, facilitate efficient communication and decision making but may suffer from single points of failure. Decentralized topologies distribute the decision-making across multiple UAVs, promoting resilience [58] and scalability but introducing challenges in synchronization and coordination. Figure 2 shows a centralized communication topology on the left and a decentralized communication topology on the right. Hybrid topologies combine centralized and decentralized elements to strike a balance between efficiency and robustness. Communication protocols and mechanisms, such as direct or indirect communication, broadcast, or multi-hop routing, determine how UAVs exchange information and collaborate within the swarm. Efficient communication protocols must address issues such as packet loss, latency, and bandwidth constraints to ensure reliable and timely data transmission. Additionally, incorporating advanced techniques like adaptive routing [59], dynamic network reconfiguration, and cognitive radio systems [60] can further enhance the swarm's communication capabilities.