Information-centric satellite networks play a crucial role in remote sensing applications, particularly in the transmission of remote sensing images. However, the occurrence of burst traffic poses significant challenges in meeting the increased bandwidth demands. Traditional content delivery networks are ill-equipped to handle such bursts due to their pre-deployed content. In this paper, we propose an optimal replication strategy for mitigating burst traffic in information-centric satellite networks, specifically focusing on the transmission of remote sensing images. Our strategy involves selecting the most optimal replication delivery satellite node when multiple users subscribe to the same remote sensing content within a short time, effectively reducing network transmission data and preventing throughput degradation caused by burst traffic expansion. We formulate the content delivery process as a multi-objective optimization problem and apply Markov decision processes to determine the optimal value for burst traffic reduction. To address these challenges, we leverage federated reinforcement learning techniques. Additionally, we use bloom filters with subdivision and data identification methods to enable rapid retrieval and encoding of remote sensing images. Through software-based simulations using a low Earth orbit satellite constellation, we validate the effectiveness of our proposed strategy, achieving a significant 17% reduction in the average delivery delay. This paper offers valuable insights into efficient content delivery in satellite networks, specifically targeting the transmission of remote sensing images, and presents a promising approach to mitigate burst traffic challenges in information-centric environments.

The low earth orbit (LEO) remote sensing satellite has a short communication time with the earth station (ES), and a large amount of remote sensing data cannot be transmitted back to the ES in time using the LEO-ES link during the communication period. Using relay satellites can indirectly increase the amount of data transmitted back from LEO. In this paper, we combine LEO- ES link and relay satellite offloading to study the problem of maximizing the amount of data transmitted back from LEO remote sensing satellites. Most of the existing methods do not consider the effect of topology change on policy. In this paper, we consider a three-layer satellite network architecture of geostationary earth orbit (GEO), LEO remote sensing satellite , and ES. We studied the problem of maximizing the amount of LEO transmitted back data under dynamic topology between layers, and proposed a transmission strategy based on a combination of LEO-ES link and GEO offload under dynamic topology. First, in order to reduce the number of link interruptions in each time slot, a Non-Uniform Time Slot Division Method (NUTSDM) based on visible relationships between layers is proposed based on discrete-time points, which helps to accurately determine the number and identity of LEOs competing under each time slot. Second, the relationship among GEOs, LEOs, and network administrators is modeled as a Stackelberg game model, and a Two-way Bargaining Game Scheme under Dynamic Topology (TWBGS-DT) is proposed to maximize the amount of data transmitted back from space. Compared with the existing methods, the experimental results confirm the effectiveness of the proposed scheme in terms of algorithm convergence speed, terms of pricing, GEO cache space allocation, and increase the data volume of LEO transmissions back by 11.5% and 8.2 times relative to the ISL-Aided strategy and GAA-FARR strategy, respectively.

With the reduction in manufacturing and launch costs of low Earth orbit satellites and the advantages of large coverage and high data transmission rates, satellites have become an important part of data transmission in air-ground networks. However, due to the factors such as geographical location and people’s living habits, the differences in user’ demand for multimedia data will result in unbalanced network traffic, which may lead to network congestion and affect data transmission. In addition, in traditional satellite network transmission, the convergence of network information acquisition is slow and global network information cannot be collected in a fine-grained manner, which is not conducive to calculating optimal routes. The service quality requirements cannot be satisfied when multiple service requests are made. Based on the above, in this paper artificial intelligence technology is applied to the satellite network, and a software-defined network is used to obtain the global network information, perceive network traffic, develop comprehensive decisions online through reinforcement learning, and update the optimal routing strategy in real time. Simulation results show that the proposed reinforcement learning algorithm has good convergence performance and strong generalizability. Compared with traditional routing, the throughput is 8% higher, and the proposed method has load balancing characteristics.