A Swarm Intelligence-Enabled UAV Communication Model for Resilient Disaster Response and Emergency Networks

Abstract

In this article, a new model of an unmanned aerial vehicle (UAV) communication system is discussed. The UAV communication model is based on swarm intelligence methods. The intention of the proposed UAV model is to support the requirements of robust disaster response and emergency networking scenarios. The suggested framework uses Artificial Intelligence (AI) in the form of deep reinforcement learning to optimize how the UAV operates through the function of planning UAV trajectories, managing energy, and adapting the delivery of dynamic coverage in extremely unpredictable environments. The proposal also uses a cluster-based method to prioritize critical users and allocate communication resources in real-time based on need. Simulation results suggest that the UAV communication model performs much better than traditional methods of deploying UAVs. Specifically, the UAV communication model demonstrated an increase in UAV coverage of 35% and a delivery rate of packets by 28% compared with standard methods of communicating via UAVs. Furthermore, system latency is reduced by 23%, resulting in a faster and more efficient method for communicating with emergency services as well. The model also improves energy usage, allowing for approximately 20% more operational time for the UAV. Additionally, the model is very adaptable to changing and dynamic conditions associated with a disaster and provides a stable link for communication even when part or the entire network becomes unavailable. Therefore, the swarm intelligence-based communication model for UAVs will serve to improve the reliability, efficiency, and resilience of temporary wireless technologies.