LAUSR

In this article, we propose a deep reinforcement learning (DRL)-based geographic routing method designed to reduce retransmission delay in mobile wireless sensor networks (MWSNs) under disaster communication scenarios. Unstable wireless channels, which are common in these challenging environments, significantly contribute to communication delays. Unlike conventional approaches that assume error-free transmissions, our method accounts for wireless channel instability and node mobility. By incorporating key factors such as topological information and transmission stability, our approach optimizes routing decisions to minimize delays while maintaining packet delivery reliability. Simulation results demonstrate that our method outperforms conventional geographic routing algorithms, achieving lower retransmission delays and higher packet delivery ratios (PDRs) across various environments.