Blockchain Assisted Industrial Data Registration and Reconstruction Management Scheme
As a typical Industrial Internet of Things (IIOT) application, three-dimensional point cloud reconstruction brings us benefits and convenience. The reconstructed mathematical models can be employed to facilitate precise quality control, which is important for the usage of the reconstructed products. Conversely, traditional reconstruction methods are characterized by inefficiency, and the errors inherent in each phase of the reconstruction chain often remain opaque and vulnerable to tampering. Hence, we propose a blockchain assisted industrial data registration and reconstruction management scheme (BIRMS). First, the tamper-proof and distributed storage characteristics of blockchain are fully utilized to ensure the authenticity and transparency of output errors throughout the reconstruction process. It is worth noting that smart contracts are designed to facilitate the management and query of on-chain data. Then, a novel swarm intelligence algorithm called EGWODA is designed to handle the issue which is low efficiency in the registration step of reconstruction. Finally, simulation results indicate the feasibility and efficiency of the BIRMS.
Authors
- Chunqiang Hu (ORCID: https://orcid.org/0000-0001-5825-2241)
- Arwa Alrawais (ORCID: https://orcid.org/0000-0002-9718-4613)
- Zewei Liu (ORCID: https://orcid.org/0000-0003-1038-3204)
- Tao Xiang (ORCID: https://orcid.org/0000-0002-9439-4623)
- Ruifeng Zhao (ORCID: https://orcid.org/0009-0007-7460-6979)
- Pengfei Hu (ORCID: https://orcid.org/0000-0002-7935-886X)
Institutions
- Prince Sattam Bin Abdulaziz University (SA)
- Chongqing University (CN)
- China Southern Power Grid (China) (CN)
- Shandong University of Science and Technology (CN)
Publication Details
- Journal
- IEEE Transactions on Network Science and Engineering
- Published
- 2025-03-04
- DOI
- https://doi.org/10.1109/tnse.2025.3547409
- Citations
- 2
- Primary Topic
- Technology and Data Analysis
- Type
- article
- Field-Weighted Citation Impact
- 6.49
Funders
- National Natural Science Foundation of China