SoftWind: Software-defined trajectory correction modelling of gust wind effects on internet of drone things using glowworm swarm optimization

Abstract

The dynamic nature of the atmosphere, especially wind gust, poses a crucial challenge to efficient and real-time drone operations. This article presents a novel MQTT based software-defined drone network for trajectory correction of drone flights in gusty wind conditions using Glowworm Swarm Optimization (GSO). By imposing the GSO to the software-defined drone network, our proposed model SoftWind has optimized the navigation and control capabilities of drones by correcting the trajectories in a gusty wind environment. We have analyzed the trajectories and convergence of drones due to wind gusts. As wind disturbances affect the trajectories of drones, we have corrected it by our trajectory correction model and evaluated the direction of the drones must fly to mitigate the wind gust and the resultant velocity compared to the no-wind environment. This study analyzed the trajectories of 100 drone flights due to various wind gust lengths (i.e., 40 m, 10 m, 6 m, and 3 m) for a fixed gust amplitude of 15 m/s and various gust amplitude (i.e., 0 m/s, 5 m/s, 15 m/s, and 40 m/s) for a fixed gust length 5 m. We observed that all the drones are converged to a single point due to low gust length (≤ 5 m) and high gust amplitude (≥ 35 m/s). It is also found that the direction of the drone must fly 28.87°. East of South to mitigate the effect of wind gusts having 10 m gust length and 15 m/s gust amplitude and the resultant velocity of the drone is 22.38 m/s. The result shows that SoftWind reduces the convergence time by 26 %-54 % as compared to other existing models.