Drone Swarm Data Transfer in 3D environments with the effect of Crosswinds using Ant Colony Optimization whilst optimising link quality, delay, and energy usage

UAV swarms generally rely on multi-hop communication systems to perform remote sensing and relay-based tasks. These communication systems involve 3D geometry, radio signal propagation, and various physical and environmental constraints. This paper presents a 3D Ant Colony Optimisation-based routing model that optimises link quality, energy consumption, and delay while accounting for obstructions and crosswinds. In the present study, the proposed model extends on previous 2D studies by incorporating variability in drone altitudes, line of sight blockages caused by buildings, roof-grazing penalties, and crosswind based propulsion energy modelling. The simulations are conducted in both a realistic urban environment as well as an open environment, without any buildings using MATLAB with the objective to optimise all parameters and constraints. The results demonstrate that urban interferences and crosswinds can alter the optimal routing path, and in some cases improve energy efficiency, and reduce delays due to the wind shielding effect from buildings and the overall shorter inter-drone distances that enables more efficient routing. Additionally, further analysis also reveals linear relationships among key parameters including link quality, delay, and energy consumption, and identifies scenarios in which urban environments outperform open environments. These findings highlight the importance of integrating a 3D environmental structure and atmospheric effects such as crosswinds into multi-objective routing models to precisely optimize UAV swarm communication systems in real-world remote sensing applications.