Delay-Aware Routing Optimization for LEO-IoT Relying on Traffic Prediction

Low Earth orbit Internet of Things (LEO-IoT) networks offer global coverage and low-latency communication, making them attractive for large-scale IoT deployments. However, rapidly varying satellite connectivity and uneven, burst ground traffic lead to unstable routing performance, resulting in fluctuating delays and increased packet loss. To address these challenges, we propose a delay-aware routing optimization (DARO) algorithm that integrates traffic prediction and distributed control. A convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM)-Attention model is developed to capture spatial–temporal traffic patterns, enhancing the accuracy of dynamic traffic prediction. A closed-form end-to-end delay model is derived to characterize the effect of routing decisions on network latency. The routing problem is then formulated as a delay and packet loss minimization task and solved using multiagent proximal policy optimization (MAPPO), enabling each satellite to adapt its routing strategy based on local observations and a shared critic. Simulation results show that DARO improves prediction accuracy by 19.05%–76.39%, reduces packet loss by 27.35%–90.76%, and lowers end-to-end delay by 2.43%–56.04%, demonstrating its effectiveness in dynamic LEO-IoT environments.