BIVO—A Decentralized Oracle Solution for Data Authenticity in Blockchain-Based IoT Networks
Bibliographic record
Abstract
Integrating blockchain technology into the Internet of Things (IoT) has revolutionized industries, enabling decentralized and reliable management of systems, while improving both efficiency and security. However, a key challenge for blockchain-based IoT solutions is ensuring the accuracy of data fed into the blockchain, known as the “blockchain oracle problem.” This work addresses this challenge by proposing the BIVO system (blockchain information verification oracles), a blockchain-based decentralized oracle for IoT networks. The system utilizes a reputation and voting mechanism suitable for both crowdsourced and semi-controlled environments. We also model the weighted voting mechanism as a stochastic game and conduct stress tests to analyze the system’s expected accuracy and cumulative payoffs under various conditions. Our findings indicate that the system achieves higher accuracy compared to nonweighted voting approaches. In semi-controlled environments, the system demonstrates resilience against up to 64% of adversarial nodes. However, under the worst conditions, malicious nodes need to control no more than 36% of the network to benefit from malicious behavior. Additionally, we implemented a prototype of the BIVO system and deployed it on both a local blockchain simulator and the public Ethereum testnet Sepolia to evaluate the cost and feasibility of blockchain integration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".