Abstract
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.
摘要
信息中心卫星网络在遥感图像传输中发挥着重要作用, 然而, 突发业务的出现在满足日益增长的带宽需求方面带来重大挑战. 传统内容传输网络 (CDN) 由于需要预先部署内容, 不具备应对此类突发流量的能力. 本文提出一种最优替代策略, 用于缓解信息中心卫星网络中的突发流量, 特别是针对遥感图像传输. 当多个用户在短时间内订阅相同的遥感图像内容时, 所提策略选择最优的替代交付卫星节点, 有效减少网络传输数据, 防止突发流量导致的吞吐量下降. 将内容传输过程公式化为一个多目标优化问题, 应用马尔可夫决策确定突发流量减少的最优值, 并利用联邦强化学习求解. 此外, 基于布隆过滤器设计了图像划分和识别方法, 快速检索编码后的遥感图像. 通过软件模拟低轨道卫星星座, 验证了所提策略的有效性, 平均交付时延减少17%. 本文为卫星网络内容高效传输, 特别是遥感图像传输, 提供宝贵见解, 并提出一种有前景的途径缓解信息中心环境中的突发流量挑战.
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Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Ziyang XING and Xiaoqiang DI designed the research. Hui QI and Jing CHEN processed the data. Ziyang XING drafted the paper. Jinhui CAO, Jinyao LIU, Xusheng LI, Zichu ZHANG, Yuchen ZHU, Lei CHEN, Kai HUANG, and Xinghan HUO helped organize the paper. Ziyang XING and Xiaoqiang DI revised and finalized the paper.
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Project supported by the National Natural Science Foundation of China (No. U21A20451)
List of supplementary materials
1 Remote sensing images
2 Dynamic network
Fig. S1 Encoded remote sensing satellite images
Fig. S2 Principle and encoding of remote sensing images into the GeoSOT grid
Fig. S3 GeoSOT subdivision and data identification
Fig. S4 Content locating and retrieving using a bloom filter
Fig. S5 Classification of users’ different demands for remote sensing images
Fig. S6 Topology switching within a satellite cycle in a dynamic network (one link)
Table S1 The modified interest package
Table S2 The modified data package
Table S3 The modified content store
Table S4 The modified pending interest table
Algorithm S1 Calculation of the same part in multiple rectangles
Algorithm S2 Iridium NEXT constellation dynamic network algorithm
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Optimal replication strategy for mitigating burst traffic in information-centric satellite networks: a focus on remote sensing image transmission
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Xing, Z., Di, X., Qi, H. et al. Optimal replication strategy for mitigating burst traffic in information-centric satellite networks: a focus on remote sensing image transmission. Front Inform Technol Electron Eng 25, 791–808 (2024). https://doi.org/10.1631/FITEE.2400025
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DOI: https://doi.org/10.1631/FITEE.2400025
Key words
- Information-centric satellite network
- Burst traffic
- Content delivery
- Federated reinforcement learning
- Mixed-integer linear programming model
- Bloom filter
- Dynamic network