Fog-Based Smart Contract Platform for Wearable IoT-Enabled Telemedicine
Bibliographic record
Abstract
Healthcare has moved towards integrating wireless technology to create and enable more services for the patients' convenience. Among the new and popular services is the formulation of wearable Internet of Things (IoT)-enabled telemedicine. However, when the number of IoT devices entering the network increases, it creates concerns about the service's ability to keep its data secure and preserve its real-time capabilities. To address these issues, in this work, we propose a fog-based IoT platform incorporating blockchain technology and smart contracts. We evaluated our design's ability to keep the real-time capabilities of wearable IoT-enabled telemedicine services through experimentation. According to the results, the introduced platform can effectively reduce the overall latency of data transactions compared to other standard network configurations. We further evaluated and observed the contributions of our design to the security of the telemedicine service. Through experimentation, we prove the feasibility of the proposed platform in addressing the highlighted issues of wearable IoT-enabled telemedicine services.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".