Rhythmic Resource State Sensing in LEO–MEO Satellite Networks Using Deep Reinforcement Learning

Satellite-enabled Internet of Things (IoT) services such as maritime sensing, aviation tracking, emergency telemetry, and wide-area monitoring require timely network-state awareness to support access control, load balancing, and resource scheduling. In Low Earth orbit (LEO) constellations, conventional resource-state reporting via ground stations is limited by short and intermittent contact windows, while large-scale LEO-to-LEO relaying is constrained by inter-satellite capacity and multi-hop latency. We investigate a LEO-medium Earth orbit (MEO) architecture where MEO satellites act as persistent aggregation nodes that collect resource-state updates from many IoT-serving LEO satellites over cross-orbit links. Due to spatially non-uniform IoT traffic and heterogeneous service rhythms, LEO satellites exhibit different resource-evolution time scales, making fixed-period sensing either waste signaling for slowly varying satellites or produce stale information for rapidly varying satellites. This creates a coupled trade-off between reporting delay and Age of Information (AoI), under limited LEO–MEO sensing capacity. To address this issue, we propose a rhythmic resource-state sensing framework that adapts each LEO satellite’s reporting cadence using a dynamic frame structure and a deep reinforcement learning policy trained by proximal policy optimization to minimize average reporting delay subject to AoI and sensing constraints. Simulations across constellation scales and traffic patterns show that the proposed approach reduces the average reporting delay by 22.5% on average and up to 25.5% compared with a heuristic baseline, while reducing the composite delay–freshness cost by 16.7% on average and up to 25.7%.