Skip to main content
Log in

Trusted consensus protocol for blockchain networks based on fuzzy inference system

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Blockchain technology with its inherent security features revolutionizes the field of distributed networks and has become one of the significant areas of research. To preserve the security features and to maintain its global state, consensus mechanisms are very essential and are performed by a set of peers in the underlying network called miners. Therefore, the miners need to be a trusted entity and their trustworthiness plays a vital role in preserving the security of the asset ledger. To ensure trusted nodes perform the consensus process, fuzzy-based trust models are robust and effective. Therefore, fuzzy inference system-based trusted consensus mechanism (FISTCON) is proposed as an effective security solution resulting in a fast and secure consensus process. The proposed scheme works in two phases. In phase 1, a fuzzy-based trust model that includes transaction history and trust feedback (F-THTF trust model) to identify trusted miners for consensus is proposed. In phase 2, a fuzzy-based effective practical byzantine fault tolerance (F-EpBFT) consensus protocol with an optimized broadcasting mechanism to decrease the communication overhead is proposed. The proposed work is implemented in the Hyperledger fabric framework, and the outcomes are thoroughly analyzed to prove the efficiency of the proposed scheme in a variety of scenarios.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Nakamoto S (2008) Bitcoin: a peer-to-peer electronic cash system. Decentralized business review, 21260. https://git.dhimmel.com/bitcoin-whitepaper/

  2. Zheng Z, Xie S, Dai HN, Chen X, Wang H (2018) Blockchain challenges and opportunities: a survey. Int J Web Grid Serv 14(4):352–375. https://doi.org/10.1504/IJWGS.2018.095647

    Article  Google Scholar 

  3. Peters G, Panayi E, Chapelle A (2015) Trends in cryptocurrencies and blockchain technologies: a monetary theory and regulation perspective. J Financ Perspect 3(3):56

    Google Scholar 

  4. Salah K, Rehman MHU, Nizamuddin N, Al-Fuqaha A (2019) Blockchain for AI: review and open research challenges. IEEE Access 7:10127–10149. https://doi.org/10.1109/ACCESS.2018.2890507

    Article  Google Scholar 

  5. Litke A, Anagnostopoulos D, Varvarigou T (2019) Blockchains for supply chain management: architectural elements and challenges towards a global scale deployment. Logistics 3(1):5

    Article  Google Scholar 

  6. Kouhizadeh M, Sarkis J (2018) Blockchain practices, potentials, and perspectives in greening supply chains. Sustainability 10(10):3652

    Article  Google Scholar 

  7. Al-Jaroodi J, Mohamed N (2019) Blockchain in industries: a survey. IEEE Access 7:36500–36515. https://doi.org/10.1109/ACCESS.2019.2903554

    Article  Google Scholar 

  8. Monrat AA, Schelén O, Andersson K (2019) A survey of blockchain from the perspectives of applications, challenges, and opportunities. IEEE Access 7:117134–117151. https://doi.org/10.1109/ACCESS.2019.2936094

    Article  Google Scholar 

  9. Kroll JA, Davey IC, Felten EW (2013) The economics of Bitcoin mining, or Bitcoin in the presence of adversaries. In: Proceedings of WEIS 2013, 11

  10. Zheng Z, Xie S, Dai H, Chen X, Wang H (2017) An overview of blockchain technology: architecture, consensus, and future trends. In: 2017 IEEE international congress on big data (BigData congress) 1(1), 557–564. https://doi.org/10.1109/BigDataCongress.2017.85

  11. Liang X, Shetty S, Tosh D, Kamhoua C, Kwiat K, Njilla L (2017) Provchain: a blockchain-based data provenance architecture in cloud environment with enhanced privacy and availability. In: 2017 17th IEEE/ACM international symposium on cluster, cloud and grid computing (CCGRID), vol 1, pp 468-477. https://doi.org/10.1109/CCGRID.2017.8

  12. Wang W, Hoang DT, Hu P, Xiong Z, Niyato D, Wang P, Kim DI (2019) A survey on consensus mechanisms and mining strategy management in blockchain networks. IEEE Access 7:22328–22370. https://doi.org/10.1109/ACCESS.2019.2896108

    Article  Google Scholar 

  13. Saleh F (2021) Blockchain without waste: proof-of-stake. Rev Financ Stud 34(3):1156–1190. https://doi.org/10.1093/rfs/hhaa075

    Article  Google Scholar 

  14. Kiayias A, Russell A, David B, Oliynykov R (2017) Ouroboros: a provably secure proof-of-stake blockchain protocol, vol 1. Springer, Cham, pp 357–388. https://doi.org/10.1007/978-3-319-63688-7_12

    Chapter  MATH  Google Scholar 

  15. Wang X, WeiLi J, Chai J (2018) The research on the incentive method of consortium blockchain based on practical byzantine fault tolerant. In: 2018 11th international symposium on computational intelligence and design (ISCID), vol 2, pp 154–156. https://doi.org/10.1109/ISCID.2018.10136

  16. Zheng K, Liu Y, Dai C, Duan Y, Huang X (2018) Model checking PBFT consensus mechanism in healthcare blockchain network. In: 2018 9th International Conference on Information Technology in Medicine and Education (ITME), vol 1, pp 877–881. https://doi.org/10.1109/ITME.2018.00196

  17. Jantzen J (1998) Tutorial on fuzzy logic. Technical University of Denmark, Department of Automation, Technical report

  18. Mehran K (2008) Takagi-sugeno fuzzy modeling for process control. Ind Autom Robot Artif Intell 262:1–31

    Google Scholar 

  19. Kamvar SD, Schlosser MT, Garcia-Molina H (2003) The eigentrust algorithm for reputation management in p2p networks. In: Proceedings of the 12th International Conference on World Wide Web, vol 1, pp 640–651

  20. Xiong L, Liu L (2004) Peertrust: Supporting reputation-based trust for peer-to-peer electronic communities. IEEE Trans Knowl Data Eng 16(7):843–857

    Article  Google Scholar 

  21. Karaoglanoglou K, Karatza H (2011) Resource discovery in a Grid system: Directing requests to trustworthy virtual organizations based on global trust values. J Syst Softw 84(3):465–478

    Article  Google Scholar 

  22. Xiaoyong LI, Xiaolin GUI (2007) Engineering trusted P2P system with fast reputation aggregating mechanism. In: 2007 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE

  23. Tajeddine A, Kayssi A, Chehab A, Artail H (2011) Fuzzy reputation-based trust model. Appl Soft Comput 11(1):345–355

    Article  Google Scholar 

  24. Li J, Liu L, Xu J (2010) A P2P e-commerce reputation model based on fuzzy logic. In: 2010 10th IEEE International Conference on Computer and Information Technology. IEEE, vol 1, pp 1275–1279

  25. Nafi KW, Hossain A, Hashem MM (2013) An advanced certain trust model using fuzzy logic and probabilistic logic theory. arXiv preprint arXiv:1303.0459

  26. Umezaki K, Spaho E, Ogata Y, Barolli L, Xhafa F, Iwashige J (2011) A fuzzy-based trustworthiness system for JXTA-overlay P2P platform. In: 2011 Tird International Conference on Intelligent Networking and Collaborative Systems. IEEE, vol 1, pp 484–489

  27. Lin H, Li Z, Huang Q (2011) Multifactor hierarchical fuzzy trust evaluation on peer-to-peer networks. Peer-to-Peer Netw Appl 4(4):376–390

    Article  Google Scholar 

  28. Shala B, Trick U, Lehmann A, Ghita B, Shiaeles S (2019) Novel trust consensus protocol and blockchain-based trust evaluation system for M2M application services. Internet Things 7:100058

    Article  Google Scholar 

  29. Wu Y, Song P, Wang F (2020) Hybrid consensus algorithm optimization: A mathematical method based on POS and PBFT and its application in blockchain. Math Probl Eng. 2020

  30. Cachin C (2016) Architecture of the hyperledger blockchain fabric. In: Workshop on distributed cryptocurrencies and consensus ledgers 310

Download references

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to R. Bala.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bala, R., Manoharan, R. Trusted consensus protocol for blockchain networks based on fuzzy inference system. J Supercomput 78, 16951–16974 (2022). https://doi.org/10.1007/s11227-022-04510-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04510-7

Keywords

Navigation