Fast Adaptive Blockchain’s Consensus Algorithm via Wlan Mesh Network

  • Xin Jiang
  • Mingzhe LiuEmail author
  • Feixiang Zhao
  • Qin Zhou
  • Ruili Wang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 895)


This paper presents a decentralised and fast adaptive block chain’s consensus algorithm with maximum voter privacy using wlan mesh network. The algorithm is suitable for consortium blockchain and private blockchain, and is written as a smart contract for Hyperledger Fabric. Unlike previously proposed blockchain’s consensus protocols, this is the first implementation that does not rely on any trusted authority to compute the tally or to protect the voter’s privacy. Instead, the algorithm is a fast adaptive protocol, and each voter is in control of the privacy of their own vote such that it can only be breached by a full collusion involving all other voters. The execution of the protocol is enforced using the consensus mechanism that also secures the Fabric blockchain. This paper tests the implementation on Fabric’s official test network to demonstrate its feasibility. Also, this paper provides a computational breakdown of its execution cost.


Consensus algorithm Blockchain Wlan mesh Hyperledger Fabric 


  1. 1.
    Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system (2008).
  2. 2.
    Higgins, S.: IBM invests \$ 200 million in blockchain-powered IoT (2016).
  3. 3.
    Clark, J., Essex, A.: CommitCoin: carbon dating commitments with bitcoin. In: Financial Cryptography and Data Security. Springer, Heidelberg (2012)Google Scholar
  4. 4.
    Ekblaw, A., Azaria, A., Halamka, J. D., Lippman, A.: A case study for blockchain in healthcare:medrec prototype for electronic health records and medical research data (2016).
  5. 5.
    International Association for Cryptologic Research: About the helios system (2016).
  6. 6.
    Adida, B.: Helios: Web-based open-audit voting. In: USENIX Security Symposium, vol. 17, pp. 335–348 (2008)Google Scholar
  7. 7.
    Nguyen, G.T., Kim, K.: A survey about consensus algorithms used in blockchain. J. Inf. Process. Syst. 14(1), 101–128 (2018)Google Scholar
  8. 8.
    Gramoli, V.: From blockchain consensus back to Byzantine consensus. Futur. Gener. Comput. Syst. (2017)Google Scholar
  9. 9.
    Tang, C., Yang, Z., Zheng, Z.L.: Game dilemma analysis and optimization of PoW consensus algorithm. Acta Autom. Sin. 43(9), 1520–1531 (2017)zbMATHGoogle Scholar
  10. 10.
    Leng, K., Bi, Y., Jing, L., Fu, H.C.: Research on agricultural supply chain system with double chain architecture based on blockchain technology. Futur. Gener. Comput. Syst. 86, 641–649 (2018)CrossRefGoogle Scholar
  11. 11.
    Wu, T., Huang, X., Zhou, L.L.: Research on blockchain consistency algorithm with state legality verification. Comput. Eng. 44(1), 160–164 (2018)Google Scholar
  12. 12.
    Jiang, P., Guo, F., Liang, K., Lai, J., Wen, Q.: Searchain: blockchain-based private keyword search in decentralized storage. Futur. Gener. Comput. Syst. (2017)Google Scholar
  13. 13.
    Kim, H.W., Jeong, Y.S.: Secure authentication-management human-centric scheme for trusting personal resource information on mobile cloud computing with blockchain. Hum. Centric Comput. Inf. Sci. 8(1), 11 (2018)CrossRefGoogle Scholar
  14. 14.
    Zhang, S.J., Cai, J., Chen, Z.H.: Byzantine consensus algorithm based on Gossip protocol. Comput. Sci. 45(2), 20–24 (2018)Google Scholar
  15. 15.
    Yuan, C., Xu, M.X., Si, X.M.: Optimization scheme of consensus algorithm based on aggregation signature. Comput. Sci. 45(2) (2018)Google Scholar
  16. 16.
    Porat, A., Pratap, A., Shah, P.: Blockchain consensus: an analysis of proof-of-work and its applications (2018).
  17. 17.
    Bach, L., Mihaljević, B., Žagar, M.: Comparative analysis of blockchain consensus algorithms. In: 41st International Convention for Information and Communication Technology, Electronics and Microelectronics (2018)Google Scholar
  18. 18.
    Feng, L., Zhang, H., Tsai, W.T., Sun, S.: System architecture for high-performance permissioned blockchains. Front. Comput. Sci., 1–15 (2018)Google Scholar
  19. 19.
    David, S., Noah, Y., Arthur, B.: The Ripple Protocol Consensus Algorithm. Ripple Labs Inc., San Francisco (2014)Google Scholar
  20. 20.
    Hao, F., Ryan, P.Y., Zielinski, P.: Anonymous voting by two-round public discussion. IET Inf. Secur. 4(2), 62–67 (2010)CrossRefGoogle Scholar
  21. 21.
    Schnorr, C.P.: Efficient signature generation by smart cards. J. Cryptol. 4(3), 161–174 (1991)CrossRefGoogle Scholar
  22. 22.
    Fiat, A., Shamir, A.: How to prove yourself: practical solutions to identification and signature problems. In: Odlyzko, A.M. (eds.) Crypto 1986. LNCS, vol. 263, pp. 186–194 (1987)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.State Key Laboratory of Geohazard Prevention and Geoenvironment ProtectionChengdu University of TechnologyChengduChina
  2. 2.Institute of Natural and Mathematical SciencesMassey UniversityAucklandNew Zealand

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