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A Decision-Making Method for Blockchain Platforms Using Axiomatic Design

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Smart Computing and Communication (SmartCom 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13828))

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Abstract

For companies using blockchain technology, it is critical to select the most suitable blockchain platform to develop enterprise applications. However, it is still a challenge for enterprises. As an important part of modern decision science, multi-criteria decision-making can well solve the problem of blockchain platform selection. Blockchain platforms integrate various blockchain technologies, and enterprises also need to consider multiple different criteria in the decision-making process. Therefore, this paper will use a heterogeneous multi-criteria decision-making method to solve the blockchain platform selection problem. First, the blockchain platform alternatives and evaluation criteria used for decision-making are identified. Second, blockchain platform alternatives are evaluated with appropriate fuzzy numbers based on defined evaluation criteria. Then, the original evaluations are consistency and normalized to obtain a normalized evaluation. Next, the improved information content formulas of the axiomatic design is proposed to obtain the information content of each normalized evaluation. Then, the weights of all evaluated criteria are obtained using the entropy weight method. Finally, the total weighted information content of each blockchain platform alternative is obtained. With validation, the decision-making model of blockchain platform proposed in this paper has a strong reference value.

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References

  1. Qiu, M., Jia, Z., et al.: Voltage assignment with guaranteed probability satisfying timing constraint for real-time multiproceesor DSP. J. Signal Process. Syst. (2007)

    Google Scholar 

  2. Qiu, M., Yang, L., Shao, Z., Sha, E.: Dynamic and leakage energy minimization with soft real-time loop scheduling and voltage assignment. IEEE TVLSI 18(3), 501–504 (2009)

    Google Scholar 

  3. Qiu, M., Xue, C., Shao, Z., Sha, E.: Energy minimization with soft real-time and DVS for uniprocessor and multiprocessor embedded systems. In: IEEE DATE Conference, pp. 1–6 (2007)

    Google Scholar 

  4. Niu, J., Gao, Y., et al.: Selecting proper wireless network interfaces for user experience enhancement with guaranteed probability. JPDC 72(12), 1565–1575 (2012)

    Google Scholar 

  5. Qiu, M., Xue, C., Shao, Z., et al.: Efficient algorithm of energy minimization for heterogeneous wireless sensor network. In: IEEE EUC, pp. 25–34 (2006)

    Google Scholar 

  6. Qiu, M., Li, H., Sha, E.: Heterogeneous real-time embedded software optimization considering hardware platform. In: ACM Symposium on Applied Computing, pp. 1637–1641 (2009)

    Google Scholar 

  7. Qiu, H., Dong, T., Zhang, T., et al.: Adversarial attacks against network intrusion detection in IoT systems. IEEE Internet Things J. 8(13), 10327–10335 (2020)

    Article  Google Scholar 

  8. Gai, K., Qiu, M., Elnagdy, S.: A novel secure big data cyber incident analytics framework for cloud-based cybersecurity insurance. In: IEEE BigDataSecurity (2016)

    Google Scholar 

  9. Gao, X., Qiu, M.: Energy-based learning for preventing backdoor attack. In: KSEM, no. 3, pp. 706–721 (2022)

    Google Scholar 

  10. Qiu, M., Chen, Z., Ming, Z., Qin, X., Niu, J.: Energy-aware data allocation with hybrid memory for mobile cloud systems. IEEE Syst. J. 11(2), 813–822 (2014)

    Article  Google Scholar 

  11. Qiu, M., Qiu, H., et al.: Secure data sharing through untrusted clouds with blockchain-enabled key management. In: SmartBlock 2020, China, pp. 11–16 (2020)

    Google Scholar 

  12. Li, J., Ming, Z., et al.: Resource allocation robustness in multi-core embedded systems with inaccurate information. J. Syst. Arch. 57(9), 840–849 (2011)

    Article  Google Scholar 

  13. Qiu, M., Qiu, H.: Review on image processing based adversarial example defenses in computer vision. In: IEEE BigDataSecurity, Baltimore, USA, pp. 94–99 (2020)

    Google Scholar 

  14. Gai, K., Zhang, Y., et al.: Blockchain-enabled service optimizations in supply chain digital twin. IEEE Trans. Serv. Comput. (2022)

    Google Scholar 

  15. Xie, M.-Y., Liu, J.: A survey on blockchain consensus mechanism: research overview, current advances, and future directions. Int. J. Intell. Comput. Cybern., 1–27 (2022)

    Google Scholar 

  16. Xie, M.-Y., Liu, J., Chen, S.-Y., Xu, G.-X., Lin, M.-W.: Primary node election based on probabilistic linguistic term set with confidence interval in the PBFT consensus mechanism for blockchain. Complex Intell. Syst. (2022). https://doi.org/10.1007/s40747-022-00857-9

  17. Liu, J., Xie, M.-Y., Chen, S.-Y., Ma, C., Gong, Q.-H.: An improved DPoS consensus mechanism in blockchain based on PLTS for the smart autonomous multi-robot system. Inf. Sci. 575, 528–541 (2021)

    Article  MathSciNet  Google Scholar 

  18. Li, Y., Gai, K., et al.: Intercrossed access controls for secure financial services on multimedia big data in cloud systems. ACM Trans. Multimedia Comput. Commun. Appl. (2016)

    Google Scholar 

  19. Liu, J., Zhao, J., Huang, H., Xu, G.: A novel logistics data privacy protection method based on blockchain. Multimedia Tools Appl. 81(17), 23867–23877 (2022)

    Google Scholar 

  20. Qiu, H., Zheng, Q, et al.: Topological graph convolutional network-based urban traffic flow and density prediction. IEEE Trans. Intell. Transp. Syst. 99 (2020)

    Google Scholar 

  21. Li, Y.-B., Song, Y., Jia, L., et al.: Intelligent fault diagnosis by fusing domain adversarial training and maximum mean discrepancy via ensemble learning. IEEE Trans. Ind. Inf. 17(4), 2833–2841 (2021)

    Article  Google Scholar 

  22. Hu, F., Lakdawala, S., Hao, Q., et al.: Low-power, intelligent sensor hardware interface for medical data preprocessing. IEEE Trans. Inf Technol. Biomed. 13(4), 656–663 (2009)

    Article  Google Scholar 

  23. Gai, K.-K., Wu, Y.-L., Zhu, L.-H., et al.: Permissioned blockchain and edge computing empowered privacy-preserving smart grid networks. IEEE Internet Things J. 6(5), 7992–8004 (2019)

    Article  Google Scholar 

  24. Qiu, M.-K., Gai, K.-K., Xiong, Z.-G.: Privacy-preserving wireless communications using bipartite matching in social big data. Future Gener. Comput. Syst. Int. J. eScience. 87, 772–781 (2018)

    Article  Google Scholar 

  25. Gai, K.-K., Fang, Z.-K., Wang, R.-L., et al.: Edge computing and lightning network empowered secure food supply management. IEEE Internet Things J. 9(16), 14247–14259 (2022)

    Article  Google Scholar 

  26. Hijazi, A.A., Perera, S., Alashwal, A.M., et al.: Enabling a single source of truth through BIM and blockchain integration. In: International Conference on Innovation, Technology, Enterprise, and Entrepreneurship 2019, pp. 24–25 (2019)

    Google Scholar 

  27. Perera, S., Nanayakkara, S., Rodrigo, M., et al.: Blockchain technology: Is it hype or real in the construction industry? J. Ind. Inf. Integr. 17 (2020)

    Google Scholar 

  28. Farshidi, S., et al.: Decision support for blockchain platform selection: three industry case studies. IEEE Trans. Eng. Manag. 67(4), 1109–1128 (2020)

    Article  Google Scholar 

  29. Büyüközkan, G., Tüfekçi, G.: A decision-making framework for evaluating appropriate business blockchain platforms using multiple preference formats and VIKOR. Inf. Sci. 571, 337–357 (2021)

    Article  MathSciNet  Google Scholar 

  30. Tang, H.-M., Shi, Y., Dong, P.-W.: Public blockchain evaluation using entropy and TOPSIS. Expert Syst. Appl. 117, 204–210 (2019)

    Article  Google Scholar 

  31. Chen, C.-H.: A novel multi-criteria decision-making model for building material supplier selection based on entropy-AHP weighted TOPSIS. Entropy 22(2) (2020)

    Google Scholar 

  32. Kumar, R., Bilga, P.-S., Singh, S.: Multi objective optimization using different methods of assigning weights to energy consumption responses, surface roughness and material removal rate during rough turning operation. J. Clean. Prod. 164, 45–57 (2017)

    Article  Google Scholar 

  33. Chen, P.-Y.: Effects of the entropy weight on TOPSIS. Expert Syst. Appl. 168 (2021)

    Google Scholar 

  34. Khan, M.J., et al.: The renewable energy source selection by remoteness index-based VIKOR method for generalized intuitionistic fuzzy soft sets. Symmetry 12(6) (2020)

    Google Scholar 

  35. Ghadikolaei, A.S., Madhoushi, M., Divsalar, M.: Extension of the VIKOR method for group decision making with extended hesitant fuzzy linguistic information. Neural Comput. Appl. 30(12), 3589–3602 (2017). https://doi.org/10.1007/s00521-017-2944-5

    Article  Google Scholar 

  36. Feng, J.-H., Xu, S.-X., Li, M.: A novel multi-criteria decision-making method for selecting the site of an electric-vehicle charging station from a sustainable perspective. Sustain. Cities Soc. 65 (2021)

    Google Scholar 

  37. Chen, X., et al.: Matching demanders and suppliers in knowledge service: a method based on fuzzy axiomatic design. Inf. Sci. 346, 130–145 (2016)

    Article  MathSciNet  Google Scholar 

  38. Büyüközkan, G., Karabulut, Y., Arsenyan, J.: RFID service provider selection: an integrated fuzzy MCDM approach. Measurement 112, 88–98 (2017)

    Article  Google Scholar 

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Authors and Affiliations

  1. Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Jun Liu, Qi Zhang, Ming-Yue Xie & Ming-Peng Chen

Authors
  1. Jun Liu
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  2. Qi Zhang
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  3. Ming-Yue Xie
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  4. Ming-Peng Chen
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Corresponding author

Correspondence to Qi Zhang .

Editor information

Editors and Affiliations

  1. Dakota State University, Madison, SD, USA

    Meikang Qiu

  2. Fudan University, Shanghai, China

    Zhihui Lu

  3. Ibaraki University, Ibaraki, Japan

    Cheng Zhang

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Liu, J., Zhang, Q., Xie, MY., Chen, MP. (2023). A Decision-Making Method for Blockchain Platforms Using Axiomatic Design. In: Qiu, M., Lu, Z., Zhang, C. (eds) Smart Computing and Communication. SmartCom 2022. Lecture Notes in Computer Science, vol 13828. Springer, Cham. https://doi.org/10.1007/978-3-031-28124-2_29

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  • DOI: https://doi.org/10.1007/978-3-031-28124-2_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28123-5

  • Online ISBN: 978-3-031-28124-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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