The traditional satellite access selection algorithm, which is used in large-scale satellite networks, has some disadvantages, such as frequent link switching, high interrupt probability, and unable to adapt to a dynamic environment. According to the periodicity of the large-scale satellite network and the prior knowledge provided by acknowledgment packages, a dynamic domain-based adaptive access algorithm (DAA) is proposed in this paper. Firstly, this algorithm divides the large-scale satellite network into different domains according to the minimum elevation angle of the Earth station (ES) and the predictable characteristics of the trajectory of the satellite. Then, the ES selects the access satellites according to the relationship between the traffic volume and the satellites’ coverage time. Finally, the ES selects the backup access satellite based on the satellites’ coverage time, the traffic volume of the ES, satellite status provided by prior knowledge, and other information. When the access satellite cannot satisfy the communication demand, the ES adaptively switches the earth-satellite link to the backup access satellite. The ES first choice of access satellite does not require interaction with the satellites, reducing the consumption of communication resources. The selection strategy of backup access satellite and the concept of virtual destination address proposed in this paper can reduce the routing overhead after switching. Through theoretical analysis and simulation results in the StarLink constellation, it is proved that this paper improves the coverage time utilization of accessing satellites and reduces the switching probability compared with the traditional access algorithm, which is more suitable for ES to access large-scale satellite networks.

A large-scale low Earth orbit (LEO) satellite network has the characteristics of a complex link environment, a large number of satellites, and the limited resources of a single satellite. Applying traditional routing algorithms has disadvantages such as high overhead, high end-to-end latency, and low message delivery rate. This paper proposes an improved delay tolerant (DTN) scheme for large-scale LEO satellite networks (LIDTN) to improve transmission efficiency and reduce the resource overhead and end-to-end latency of large-scale satellite networks. This scheme improves the network performance in three aspects: next hop selection, congestion control mechanism, and acknowledgment mechanism. For the next hop selection, we propose an equivalent distance and priori knowledge-based forwarding strategy (EPFS), which has the advantages of low overhead, loop avoidance, and fast convergence. For congestion control, we put forward an emergency function-based bundle drop algorithm (EBDA). For acknowledging, we propose the virtual acknowledgment algorithm (VAA) by combining the characteristics of many path hops and high link disruption rates in large-scale constellations. Finally, we simulate and verify the LIDTN scheme on the OneWeb constellation. The results show that the LIDTN scheme is suitable for large-scale constellations, the EPFS algorithm can reduce the network overhead during data transmission, EBDA can reduce the bundle drop rate, and VAA can reduce the end-to-end latency. LIDTN provides a new solution for large-scale constellation communication.

Traditional routing techniques face considerable challenges in large-scale low Earth orbit (LEO) satellite networks for Internet of Things (IoT) data backhaul, such as slow convergence speed, excessive end-to-end hops, and high latency. Recent intelligent routing algorithms have achieved some progress in improving routing performance; however, significant potential remains in enhancing convergence efficiency, hop-count management, and latency optimization. To overcome the insufficient adaptability of existing approaches in large-scale LEO constellation environments, this article proposes a collaborative routing algorithm that integrates $Q$ -learning and game theory, termed collaborative $Q$ -learning and game-theoretic routing (CoQGR). By fully exploiting satellite network topology characteristics, CoQGR deeply integrates and adapts $Q$ -learning models with game-theoretic models. Specifically, in the $Q$ -learning module, a greedy strategy dynamically optimizes the $Q$ -table to accelerate convergence and establish basic routing paths. In the game-theoretic module, dynamic coalitions among satellites along the basic paths are formed based on maximum trusted communication distances, thereby enhancing the initial routes. Ultimately, under an identical maximum trusted distance constraint, unnecessary relay satellites are bypassed, reducing routing hops and further decreasing end-to-end latency. Simulation experiments are conducted based on the Starlink constellation, comparing CoQGR with recent $Q$ -learning routing algorithms designed for large-scale constellations. The simulation results indicate that CoQGR significantly outperforms existing schemes by reducing routing convergence time, end-to-end hop counts, and average transmission latency by approximately 50%, 55.84%, and 41.39%, respectively.