A Blockchain based Authentication Scheme for Mobile Data Collector in IoT
Bibliographic record
Abstract
Our paper proposes a new device authentication scheme for mobile sensor node called Mobile Data Collector (MDC). Moreover, to validate the data brought by the MDC to the base station (BS), we validate it and then store it. To solve MDC authentication between multiple devices, we proposed blockchain scheme to provide more ease, communication and security between different devices. For this to happen, the last MDC authentication (meaning the first time the information is gathered) is performed by the CH'S first encounter with the classic authentication, and here the protocol accepts or rejects the MDC. Once the CH has authenticated the MDC, CH sends a transaction to the blockchain to verify the legality of the MDC access. Then, when the MDC requests the collected data from another CH in the network, at this point, any CH verifies the trust of the MDC by communicating with the blockchain. Hence, the proposed scheme is as safe as we claim. More specifically, in the proposed protocol for Blockchain Security IoT (Block_MDC) is to provide authentication between the Mobile Data Set (MDC), the head of the group and the member nodes of the WSN. We evaluate the performance of our protocol using simulations using MATLAB. The results confirm that the Block_MDC protocol is robust, efficient, and offers lower power consumption and fast computing time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".