Abstract
The bulk commodity is a major strategic resource of the country, and related trade markets are booming. However, due to channel isolation and information barriers, the trade platforms also have risks and industry chaos, which make the bulk commodity trade process suffer from information asymmetry, difficulty in choosing a suitable commodity, difficulty in commodity pricing, and lack of trust in the trade platform. To cope with these challenges, we propose BBCT, a Blockchain-based Bulk Commodity Trade system, to achieve credible and fair bulk commodity trade. The process of multi-party trade is divided into four stages: matching, negotiation based on time-constrained Bayesian learning, decision-making based on the technique for order preference by similarity to ideal solution and signing. We design a “negotiation-signing” dual-channel blockchain architecture, so that buyers and sellers can complete the negotiation and signing process coordination on the blockchain. We finally verify the efficiency and reliability of BBCT through experiments.
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References
Qiu, H., Qiu, M., Memmi, G., Ming, Z., Liu, M.: A dynamic scalable blockchain based communication architecture for IoT. In: Qiu, M. (ed.) SmartBlock 2018. Lecture Notes in Computer Science, vol. 11373, pp. 159–166. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05764-0_17
Gai, K., et al.: Differential privacy-based blockchain for industrial Internet-of-Things. IEEE Trans. Industr. Inf. 16(6), 4156–4165 (2019)
Qiu, M., Liu, X., Qi, Y., Zhao, H., Liu, M.: AI enhanced blockchain (II). In: The 3rd International Conference on Smart BlockChain (SmartBlock), pp. 147–152 (2020)
Pan, W., et al.: Application of blockchain in asset-backed securitization. In: IEEE 6th International Conference on Big Data Security (BigDataSecurity) (2020)
Tian, Z., et al.: Block-DEF: a secure digital evidence framework using blockchain. Inf. Sci. 491, 151–165 (2019)
Dekker, P., Andrikopoulos, V.: Automating bulk commodity trading using smart contracts. In: IEEE International Conference on Decentralized Applications and Infrastructures (DAPPS) (2020)
hyperledger-fabricdocs main documentation. https://hyperledger-fabric.readthedocs.io/en/latest/. Accessed 30 Oct 2021
Yang, J., Lu, Z., Wu, J.: Smart-toy-edge-computing-oriented data exchange based on blockchain. J. Syst. Archit. 87, 36–48 (2018)
Eshragh, F., Shahbazi, M., Far, B.: Real-time opponent learning in automated negotiation using recursive Bayesian filtering. Expert Syst. Appl. 128, 28–53 (2019)
Gabriel, S., Riyanarto, S.: AHP-TOPSIS for analyzing job performance with factor evaluation system and process mining. Telkomnika (Telecom. Com. Elec. Cont.) 17(3), 1344–1351 (2019)
Saaty, T.L.: The Analytic Hierarchy Process. Mc Graw-Hill Company, New York (1980)
Bapi, D., Duy, D.S., Luis, M., Mark, G.: An evolutionary strategic weight manipulation approach for multi-attribute decision making: TOPSIS method. Int. J. Approx. Reason. 129, 64–83 (2021)
Vavrek, R.: Evaluation of the Impact of selected weighting methods on the results of the TOPSIS technique. Int. J. Inform. Tech. Decis. Making 18(06), 1821–1843 (2019)
Rubinstein, A.: Perfect equilibrium in a bargaining model. Econometrica 50(1), 97–109 (1982)
Torstensson, P.: An n-person Rubinstein bargaining game. Int. Game Theory Rev. 11(1), 111–115 (2009)
Al-Saggaf, A., Ghouti, L.: Efficient abuse-free fair contract-signing protocol based on an ordinary crisp commitment scheme. IET Inf. Secur. 9(1), 50–58 (2015)
Peyman, F.: Negotiation decision functions for autonomous agents. Robot. Auton. Syst. 24(3–4), 159–182 (1998)
Wang, Y., Chardonnet, J., Merienne, F.: Enhanced cognitive workload evaluation in 3D immersive environments with TOPSIS model. Int. J. Hum. Comput. Stud. 147, 102572 (2021)
Acknowledgment
The work of this paper is supported by the National Key Research and Development Program of China (2019YFB1405000), National Natural Science Foundation of China under Grant (No. 92046024, 61873309), and Shanghai Science and Technology Innovation Action Plan Project under Grant (No. 19510710500, and No. 18510732000).
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Yang, J., Lu, Y., Lu, Z., Wu, J., Zhao, H. (2022). BBCT: A Smart Blockchain-Based Bulk Commodity Trade System. In: Qiu, M., Gai, K., Qiu, H. (eds) Smart Computing and Communication. SmartCom 2021. Lecture Notes in Computer Science, vol 13202. Springer, Cham. https://doi.org/10.1007/978-3-030-97774-0_17
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DOI: https://doi.org/10.1007/978-3-030-97774-0_17
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