Welcome to Francis Academic Press

Academic Journal of Computing & Information Science, 2022, 5(5); doi: 10.25236/AJCIS.2022.050503.

Dynamic Transaction Confirmation Double-chain System Based on Alliance Chain


Nigang Sun, Lidong Yao, Qiaosheng Hu

Corresponding Author:
Lidong Yao

School of Computer and Artificial Intelligence, Changzhou University, Changzhou, China


In recent years, the high performance, strong controllability and partial decentralization of the alliance chain make it have a wide range of application scenarios and development potential. However, restricted by the consensus algorithm, the alliance chain cannot fully meet the needs of practical applications at present. Aiming at the poor stability of the Byzantine Fault Tolerance (PBFT) consensus algorithm, this paper proposes a fast consensus algorithm, which reduces the message size by optimizing the transaction verification process and dynamically adjusting the number of verification nodes. Combined with the reward and punishment system of points, a dynamic transaction confirmation double-chain system is designed. Compared with the alliance chain system implemented by the PBFT algorithm, the system has stronger stability in the case of multiple nodes, and the TPS is increased by at least 21% under the same number of nodes. It is suitable for some nodes with frequent access and poor network.


Alliance chain; Consensus algorithm; Point reward and punishment system; PBFT; TPS

Cite This Paper

Nigang Sun, Lidong Yao, Qiaosheng Hu. Dynamic Transaction Confirmation Double-chain System Based on Alliance Chain. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 5: 17-25. https://doi.org/10.25236/AJCIS.2022.050503.


[1] Nakamoto S. Bitcoin: A peer-to-peer electronic cash system [J]. Decentralized Business Review, 2008: 21260.

[2] Shen Xin, Pei Qing-qi, Liu Xue-feng. A Review of Blockchain Technology [J]. Journal of Network and Information Security, 2016, 2(11): 11-20.

[3] Lamport L. Paxos made simple [J]. ACM Sigact News, 2001, 32(4): 18-25.

[4] Ongaro D, Ousterhout J. In search of an understandable consensus algorithm [C]// 2014 {USENIX} Annual Technical Conference ({USENIX}{ATC} 14). 2014: 305-319.

[5] Bessani A, Sousa J, Alchieri E E P. State machine replication for the masses with BFT-SMART [C]// 2014 44th Annual IEEE/IFIP International Conference on Dependable Systems and Networks. IEEE, 2014: 355-362.

[6] Rocket T. Snowflake to avalanche: A novel metastable consensus protocol family for cryptocurrencies [J]. Available [online]. [Accessed: 4-12-2018], 2018.

[7] Aublin P L, Mokhtar S B, Quéma V. Rbft: Redundant byzantine fault tolerance [C]//2013 IEEE 33rd International Conference on Distributed Computing Systems. IEEE, 2013: 297-306.

[8] Castro M, Liskov B. Practical byzantine fault tolerance [C]// OSDI. 1999, 99(1999): 173-186.

[9] Delegated Byzantine Fault Tolerance.https://docs.neo.org/v2/docs/zh-cn/basic/technology/dbft.html. Jan. 2021

[10] Verifiable Byzantine Fault Tolerance.https://github.com/ontio/documentation/blob/master/vbft-intro/vbft-intro.md.Jan.2021

[11] Luu L, Narayanan V, Zheng C, et al. A secure sharding protocol for open blockchains [C]// Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. 2016: 17-30.

[12] Liu J, Li W, Karame G O, et al. Scalable byzantine consensus via hardware-assisted secret sharing [J]. IEEE Transactions on Computers, 2018, 68(1): 139-151.

[13] Xu Zhi-li, Feng Hua-min, Liu Biao. An Improved PBFT Efficient Consensus Mechanism Based on Credit [J]. Computer Application Research, 2019, 36(9): 2788-2791.

[14] Cao Zhao-lei. A Consensus Mechanism for Consortium Chains [J]. Space Network Security, 2019, 10(1): 1-6.

[15] Leng Ji-dong, Lv Xue-qiang, Jiang Yang, et al. Research Review on Consensus Mechanism of Consortium Chain [J]. Data Analysis and Knowledge Discovery, 2021, 5(1): 56-65.

[16] Zheng Min, Wang Hong, Liu Hong, et al. Research Review of Blockchain Consensus Algorithms [J]. Information Network Security, 2019, 19(7): 8-24.

[17] Lu Ge-hao, Xie Li-hong, Li Xi-yu. Comparative Research on Blockchain Consensus Algorithms [J]. Computer Science, 2020, 47(6A): 332-339.

[18] Jin Shi-xiong, Zhang Xiao-dan, Ge Jing-guo, et al. Research Review of Blockchain Consensus Algorithms [J]. Journal of Information Security, 2021, 6(2): 85-100.