Remote and Disaster Zone Communication Bridge during the Emergency Response of UAVs

Abstract

In recent years, long-term communication systems for emergencies using UAVs have been developing rapidly in particular situations, such as disasters in remote regions. Previous machine learning approaches exhibit several limitations, including limited communication range, data loss in transmission systems, limited bandwidth availability, and abrupt communication failures, which collectively hinder overall system performance. To address this limitation, propose a hybrid, optimization-based UAV-assisted communication framework that integrates Federated Learning with swarm intelligence algorithm. The disaster-aware UAV deployment uses Federated Learning for decision-making to identify critical communication zones. A hybrid algorithm combining federated reinforcement learning with a graph attention-based UAV communication framework for consistent, low-latency data communication. The UAV network's lifecycle securities constant communication, energy efficient resource allocation and load balancing. In experiment analysis, 74.2% reduction in end-to-end latency (248 ms to 62 ms), 53.8% reduction in energy consumption, and a packet delivery ratio of up to 94% under varying network densities. The proposed system delivers reliable, scalable, and intelligent communication for emergency response in remote and disaster-affected areas.