JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2020, Vol. 55 ›› Issue (3): 43-50.doi: 10.6040/j.issn.1671-9352.2.2019.142

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A blockchain consensus mechanism based on voting rights competition

Yu-bo SONG1,2(),Shi-qi ZHANG1,2,Rui SONG1,2   

  1. 1. School of Cyber Science and Engineering, Jiangsu Key Laboratory of Computer Networking Technology, South East University, Nanjing 211189, Jiangsu, China
    2. Purple Mountain Laboratories, Nanjing 211189, Jiangsu, China
  • Received:2019-09-02 Online:2020-03-20 Published:2020-03-27


Under the consensus mechanism in the blockchain system, all participants agreed on the issue of block ownership and the value of the transaction. Based on the new architecture design of the forging committee and the forging group system, a competition-based equity certification (CPoS) consensus mechanism is proposed, which can quickly remove the fork under the premise of ensuring decentralization. Through prototype experiments, the block and trading activities can be completed quickly with a small delay.

Key words: blockchain, consensus mechanism, proof of stake, forging commissioner

CLC Number: 

  • TP311


Forged group behavior under ideal conditions"


The forgeer with the second highest voting rights creates a new block"


Bifurcation is eliminated"


Side chain fork"


Cross-chain transaction specification"


Transaction confirmation time and block speed"

1 SIDHU J. Syscoin: a peer-to-peer electronic cash system with blockchain-based services for E-business[C]//2017 26th International Conference on Computer Communication and Networks (ICCCN). New York: IEEE, 2017: 1-6.
2 BUTERIN V . A next-generation smart contract and decentralized application platform[J]. White Paper, 2014, 3: 37.
3 VUKOLIĆ M. The quest for scalable blockchain fabric: proof-of-work vs. BFT replication[M]// Open Problems in Network Security. [S.l.]: Springer International Publishing, 2016: 112-125.
4 BIRYUKOV A , PUSTOGAROV I . Proof-of-work as anonymous micropayment: rewarding a tor relay[M]. Berlin: Springer, 2015: 445- 455.
5 FULLMER D, MORSE A S. Analysis of difficulty control in bitcoin and proof-of-work blockchains[C]// 2018 IEEE Conference on Decision and Control (CDC). New York: IEEE, 2018: 5988-5992.
6 SHI N . A new proof-of-work mechanism for bitcoin[J]. Financial Innovation, 2016, 2: 31.
doi: 10.1186/s40854-016-0045-6
7 GILBOA A, SHTEINGART Z, LEVIN K, et al. Method and system for reducing power consumption in bitcoin mining via data input hopping: U.S. Patent Application 15/513, 177[P]. 2017-10-19.
8 GERVAIS A, KARAME G O, WVST K, et al. On the security and performance of proof of work blockchains[C]// Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security-CCS′16. New York: ACM Press, 2016.
9 SAYADI S, BEN REJEB S, CHOUKAIR Z. Blockchain challenges and security schemes: a survey[C]// 2018 Seventh International Conference on Communications and Networking (ComNet). New York: IEEE, 2018.
10 CHEN T, LI X Q, WANG Y, et al. An adaptive gas cost mechanism for ethereum to defend against under-priced DoS attacks[M]// Information Security Practice and Experience. [S.l.]: Springer International Publishing, 2017: 3-24.
11 KIM T . On the transaction cost of Bitcoin[J]. Finance Research Letters, 2017, 23: 300- 305.
doi: 10.1016/
12 ALLEN D W E , BERG C , LANE A M , et al. Cryptodemocracy and its institutional possibilities[J]. The Review of Austrian Economics, 2018, 11: 1- 12.
doi: 10.1007/s11138-018-0423-6
13 O'DWYER K J, MALONE D. Bitcoin mining and its energy footprint[C]// 25th IET Irish Signals & Systems Conference 2014 and 2014 China-Ireland International Conference on Information and Communities Technologies. Limerick, Ireland: Institution of Engineering and Technology, 2014: 280-285.
14 ROSENFELD M . Proof of activity: extending bitcoin's proof of work via proof of stake [Extended Abstract]y[J]. ACM Sigmetrics Performance Evaluation Review, 2014, 42 (3): 34- 37.
doi: 10.1145/2695533.2695545
15 DUONG T, CHEPURNOY A, FAN L, et al. Twinscoin: a cryptocurrency via proof-of-work and proof-of-stake[C]// Proceedings of the 2nd ACM Workshop on Blockchains, Cryptocurrencies, and Contracts. [S.l.]: ACM, 2018: 1-13
16 HAZARI S S , MAHMOUD Q H . Comparative evaluation of consensus mechanisms in cryptocurrencies[J]. Internet Technology Letters, 2019, 2 (3): 1- 6.
17 ATTARAN M , GUNASEKARAN A . Blockchain principles, qualities, and business applications[M]. New York: Springer, 2019: 13- 20.
18 PILLAI B, BISWAS K, MUTHUKKUMARASAMY V. Blockchain interoperable digital objects[C]// International Conference on Blockchain. New York: Springer, 2019: 80-94.
19 GUERAR M, VERDERAME L, MERLO A, et al. Blockchain-based risk mitigation for invoice financing[C]// Proceedings of the 23rd International Database Applications & Engineering Symposium on-IDEAS ′19. New York: ACM Press, 2019.
20 GERVAIS A, KARAME G O, WVST K, et al. On the security and performance of proof of work blockchains[C]// Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security-CCS′16. New York: ACM Press, 2016.
21 KWON Y, KIM D, SON Y, et al. Be selfish and avoid dilemmas: fork after withholding (faw) attacks on bitcoin[C]// Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. New York: ACM, 2017: 195-209.
22 STOYKOV L, ZHANG K W, JACOBSEN H A. VIBES: fast blockchain simulations for large-scale peer-to-peer networks[C]// Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference on Posters and Demos-Middleware ′17. New York: ACM Press, 2017.
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[2] JIANG Xue-lian, SHI Hong-bo*. The learning algorithm of a generative and discriminative combination classifier[J]. J4, 2010, 45(7): 7 -12 .
[3] HUANG Xian-li,LUO Dong-mei. Feature impprtance study on  transfer learning of  sentiment  text  classification[J]. J4, 2010, 45(7): 13 -17 .
[4] LI Yong-ming1, DING Li-wang2. The r-th moment consistency of estimators for a semi-parametric regression model for positively associated errors[J]. J4, 2013, 48(1): 83 -88 .
[5] YU Xiu-qing. (σ,τ)-expansion model of P-sets and its properties#br#[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(04): 90 -94 .
[6] ZHANG Ya-dong1, LI Xin-xiang2, SHI Dong-yang3. Superconvergence analysis of a nonconforming finite element for #br# strongly damped wave equations[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(05): 28 -35 .
[7] ZHANG Ling ,ZHOU De-qun . Research on the relationships among the λ fuzzy measures, Mbius representation and interaction representation[J]. J4, 2007, 42(7): 33 -37 .
[8] XIE Shu-tao,SONG Xiao-yanAntimicrobial activities of Trichokonins: Peptaibollike antimicrobial peptides produced by Trichoderma koningii[J]. J4, 2006, 41(6): 140 -144 .
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[10] XU Guang-zhu1, LIU Ming2, REN Dong1, MA Yi-de3, LIU Xiao-li1. Multi-region image segmentation based on pulse coupled neural network[J]. J4, 2010, 45(7): 86 -93 .