Satellite network-optimized Dynamic Scoped Hierarchical Raft for blockchain consensus

Jingyi Li , Rui Huang , Nan Shi , Hongjian Weng , Qiangyu Wang , Guo Li , Yufei He , Chunqi Tian

Autonomous Intelligent Systems ›› 2026, Vol. 6 ›› Issue (1) : 4

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Autonomous Intelligent Systems ›› 2026, Vol. 6 ›› Issue (1) :4 DOI: 10.1007/s43684-026-00126-3
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Satellite network-optimized Dynamic Scoped Hierarchical Raft for blockchain consensus
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Abstract

Blockchain has achieved widespread application in various fields due to its decentralized nature, data immutability, and transparency. Particularly, its integration with satellite networks provides a more secure and efficient solution for cross-regional, high-speed transmission, and reliable communication. However, challenges such as network fluctuations, performance bottlenecks, and leader election issues arise in this context, primarily due to the uneven computational power distribution of heterogeneous devices in satellite networks, as well as bandwidth limitations, signal delays, and instability. To address these challenges, this paper proposes a Dynamic Scoped Hierarchical Raft algorithm based on the network performance and computational power differences of nodes. The algorithm establishes consensus groups and restricts the pool of eligible leader candidates, thereby enhancing the adaptability of blockchain in satellite networks. Furthermore, by introducing different consensus subgroups, the scalability of the blockchain system is improved. Experimental results show that, compared to the traditional Raft algorithm, the proposed algorithm achieves a 65% increase in average throughput, a 12% reduction in latency, and a 71% reduction in leader election time, with a significantly lower chance of leader node failure when nodes drop out due to network instability.

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Blockchain / Consensus Alogrithm / Satellite network

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Jingyi Li, Rui Huang, Nan Shi, Hongjian Weng, Qiangyu Wang, Guo Li, Yufei He, Chunqi Tian. Satellite network-optimized Dynamic Scoped Hierarchical Raft for blockchain consensus. Autonomous Intelligent Systems, 2026, 6(1): 4 DOI:10.1007/s43684-026-00126-3

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