Multi-Scale Planning and Parallel Flow Allocation in Satellite-Terrestrial Integrated Networks

Data aggregation is a cornerstone technology for cross domain data processing and computing within Satellite-Terrestrial Information Networks by collecting and integrating information from multiple satellites. Current approaches primarily focus on static topologies or distributed aggregation strategies, neglecting critical issues such as network resource competition, node heterogeneity and redundant data transmission across multiple nodes. To tackle these challenges, we develop a multi-scale dynamic aggregation model that performs multi-objective path planning to balance delay, resource usage, and energy cost. Then we propose a resource-awared convex optimization algorithm that quantifies inter-satellite data redundancy during transmission and dynamically scales each link's capacity to only transmit non-redundant information, achieving efficient flow scheduling in heterogeneous resource and parallel environments. Experimental results demonstrate that the proposed method reduces communication cost by an average of 32.9%, shortens task execution time by up to 58.2%, and decreases total energy consumption by approximately 35.6\\% across six benchmark datasets compared with selected algorithms.