Blockchain-based IoT Device Security
Bibliographic record
Abstract
Due to the quick increase of IoT devices, they lack the authentication standards and administration needed to keep user data secure. Hackers could cause significant infrastructure harm by infiltrating a wide spectrum of IoT devices. Blockchain use in IoT technology guarantees trust and authentication across all IoT elements, resulting in IoT security. Blockchain is a decentralized, distributed, and shared database that enables the creation of decentralized apps. Traceability, openness, immutability, and fault tolerance are some of the qualities of this technology that help maintain data privacy in IoT scenarios and thus create a safe environment. We look at a potential strategy for securely controlling IoT devices,i.e., devices connected to the internet using smart contracts on the blockchain in this study. This paper demonstrates how the proposed system comprising of a blockchain and smart contracts work efficiently in concurrence to avoid tampering by unauthorized parties. We have employed web3 library to control the linked devices by implementing Ethereum nodes (second most popular blockchain) on Raspberry Pi simulations and node.js.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".