Private Blockchain-Based Fog-IoT Platform for Wearables in Healthcare
Bibliographic record
Abstract
The introduction of wearable Internet of Things (IoT) devices in healthcare services has recently increased due to their convenience and effectiveness. As a result, more devices are added to the network daily, creating concerns about data security and privacy, network management, and device standardization. In response, we propose a fog-based IoT platform to address these issues. We use the fog’s distributed structure to allow a more systematic organization of wearable IoT devices. We reinforce the security and privacy of the data within the network by implementing a private blockchain design. Each addition aims to improve the organization of wearable IoT devices within the healthcare network and patient data protection. To examine its performance, we designed a testbed to evaluate the advantages and feasibility of our platform. The results suggest that the decentralized design is viable over unbalanced networks in terms of network size and growing transmitted data overhead.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".