TwinIpfsChain: Ensuring Decentralized Data Integrity in Real-Time Digital Twins for Industry 5.0 Using Blockchain and IPFS
Bibliographic record
Abstract
Digital twins have become essential to Industry 5.0, enabling continuous synchronization between physical systems and their digital counterparts. They support predictive maintenance, operational efficiency, and data-driven decision-making. Several solutions for ensuring integrity of static data are available. However, ensuring the integrity and trustworthiness of data exchanged in real time remains a major challenge, particularly in decentralized industrial environments prone to tampering, high latency, and performance bottlenecks. This paper presents TwinIpfsChain, a decentralized architecture that combines the Solana blockchain and IPFS to guarantee secure, verifiable, and scalable data management for digital twins. The proposed approach stores sensor data hashes on-chain, while the full data is preserved in InterPlanetary File System (IPFS). A smart contract manages automatic integrity verification and enforces access control.TwinIpfsChain ensures real-time synchronization of high-frequency sensor data for digital twins in Industry 5.0. We implemented and evaluated the system under varying transaction loads $(\mathbf{1, 0 0 0 - 1 0, 0 0 0})$ to assess performance across key metrics: latency, throughput, energy consumption, and security. Our results show that TwinIpfsChain achieves low latency (average 218 ms), high throughput (up to 26,457 TPS), low power usage (under 13 W), and $100 \%$ detection of tampering, hash manipulation & replay attacks. Transaction costs remain negligible 0.84 USD per 1,000 transactions making our solution viable for resource constrained industrial deployments. Compared to existing blockchain-based models, TwinIpfsChain offers stronger real-time guarantees, improved scalability, and robust protection against data integrity threats, positioning it as a practical and high-performance solution for digital twin applications in Industry 5.0.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".