Resource allocation strategy for satellite Internet integrating communication and remote sensing based on resource-time-varying graphs

To address the contradiction between real-time data backhaul demands and limited onboard resources in integrated sensing and communication (ISAC) constellations, a resource time-evolving graph (RTEG) model was constructed. A unified structural representation of dynamic topology and multi-dimensional resources, including energy, storage, and bandwidth, was achieved. To realize the joint optimization of maximizing network throughput and minimizing satellite transmission energy consumption, the joint observation-transmission-storage scheduling problem was mapped to the minimum cost maximum flow problem in graph theory. Subsequently, an energy-efficient incremental maximum flow algorithm (EEIMFA) was proposed. Reverse links were introduced to dynamically adjust intra-slot link selection, and temporal vertical links were utilized to implement cross-slot data scheduling, whereby path decisions minimizing energy consumption were attained while approaching maximum throughput. Simulation results demonstrate that the data transmission capability of the ISAC constellation is superior to that of single-function satellite configurations. Additionally, the proposed algorithm shows to lead in key indicators such as network energy consumption, link utilization, and runtime, with energy consumption being 10≈ 18 lower than that of the EERA algorithm in all test scenarios. Consequently, this study provides technical guarantees for the efficient and low-energy operation of ISAC constellations.