Data Acquisition and Monitoring Scheme for Satellite Network with Time-Varying Topology

As human beings further explore space, satellite systems have become an indispensable tool for human society. However, managing satellite networks, especially in edge cloud computing scenarios, presents unique challenges due to its time-varying nature. To address these challenges, we propose a novel data acquisition and monitoring scheme specifically designed for satellite network environments. Our scheme leverages eBPF (extended Berkeley Packet Filter) technology for real-time monitoring data collection and utilizes a spatial-temporal graph representation to capture the dynamic topology of the satellite network. The Max-Min Fair (MMF) algorithm is employed to compute efficient routing paths, while data preprocessing techniques are applied to alleviate data transmission pressure. The scheme is designed to support user-defined monitoring targets and enables active detection and monitoring of application processes based on specific requirements. To evaluate the effectiveness of our proposed scheme, we conduct a comparative analysis against the Shortest Path on the Spatial-Temporal graph (SPST) algorithm and the Energy-Aware Muitipath (EAMP) algorithm. Experimental results demonstrate the superiority of the MMF algorithm, exhibiting a significant 32.6% and 21.5% improvement in data transmission volume compared to the SPST and EAMP algorithms, respectively. Moreover, the utilization of the MMF algorithm reduces the average satellite bandwidth utilization by 37.1 % and 41.2% throughout the transmission cycle, surpassing the performance of the SPST and EAMP alogrithms.