Data storage mechanism of industrial IoT based on LRC sharding blockchain

Yongjun Ren, Xinyu Liu, Pradip Kumar Sharma, Osama Alfarraj, Amr Tolba, Shenqing Wang, Jin Wang* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
4 Downloads (Pure)

Abstract

With the rapid development of Industry 4.0, the data security of Industrial Internet of Things in the Industry 4.0 environment has received widespread attention. Blockchain has the characteristics of decentralization and tamper-proof. Therefore, it has a natural advantage in solving the data security problem of Industrial Internet of Things. However, current blockchain technologies face challenges in providing consistency, scalability and data security at the same time in Industrial Internet of Things. To address the scalability problem and data security problem of Industrial Internet of Things, this paper constructs a highly scalable data storage mechanism for Industrial Internet of Things based on coded sharding blockchain. The mechanism uses coded sharding technology for data processing to improve the fault tolerance and storage load of the blockchain to solve the scalability problem. Then a cryptographic accumulator-based data storage scheme is designed which connects the cryptographic accumulator with the sharding nodes to save storage overhead and solve the security problem of data storage and verification. Finally, the scheme is proved to be security and the performance of the scheme is evaluated.

Original languageEnglish
Article number2746
Number of pages12
JournalScientific Reports
Volume13
DOIs
Publication statusPublished - 16 Feb 2023

Bibliographical note

Funding Information:
This work is supported by the National Natural Science Foundation of China (Nos. 62072249, 62072056, 62076125). This work is also funded by the National Science Foundation of Hunan Province (2022JJ2029), and Hunan Provincial Key Research and Development Program (Grant No. 2022GK2019). This work is also funded by the Researchers Supporting Project number (RSP2023R102) King Saud University, Riyadh, Saudi Arabia. This work was also supported by the National Key R & D Program of Guangdong Province (Grant No. 2020B0101090002), and the Natural Science Foundation of Jiangsu Province (Grant Nos. BK20200418, BE2020106).

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