Open Access

ARTICLE

CI-Block: A Blockchain System for Information Management of Collaborative Innovation

Ruhao Ma^1,*, Fansheng Meng¹, Haiwen Du^2,3

1 School of Economics and Management, Harbin Engineering University, Harbin, 150001, China
2 School of Astronautics, Harbin Institute of Technology, Harbin, 150001, China
3 The Insight Centre for Data Analytics, School of Computer Science, University College Dublin, Dublin, Ireland

* Corresponding Author: Ruhao Ma. Email:

Intelligent Automation & Soft Computing 2022, 33(3), 1623-1637. https://doi.org/10.32604/iasc.2022.026748

Received 04 January 2022; Accepted 15 February 2022; Issue published 24 March 2022

Abstract

Blockchain technology ensures the security of cross-organizational data sharing in the process of collaborative innovation. It drives the development of collaborative innovation in discrete manufacturing to intelligent innovation. However, collaborative innovation is a multi-role, networked, and open resource-sharing process. Therefore, it is easy to form information barriers and increase the risk of cooperation between organizations. In this paper, we firstly analyze the blockchain-based information management models in the traditional discrete manufacturing collaborative innovation process. Then, we found that in the process of industry-university-research (IUR) collaborative innovation, consensus servers maintain too many connections due to the high latency between them, which leads to lower consensus performance and efficiency. To solve this problem, we proposed the ependency nalysis (DA) model, which separates and assembles transactional data and non-transactional data into different blocks by analyzing read_set and write_set of transactions. Besides, we propose the ut-of-rder (OORaft) model to allow non-transactional blocks to be copied in parallel, which can also prioritize transactional blocks. Finally, we implement the blockchain model in the discrete manufacturing scenario based on Hyperledger Fabric. The experimental results show that our model improves the transactions per second (TPS) performance of 1.9X-3.7X and reduces transactional data committing latency by more than 40% in the target scenario.

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Keywords

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