A Cloud-Fog Based Architecture for IoT Applications Dedicated to Healthcare
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cloud-Fog computing architectures are new paradigms designed to add advantages to the existing architectures for Internet of Things (IoT). This paper proposes an inter-operable cloud-fog based IoT architecture for healthcare. It describes its architecture, environmental context, and user context. The proposed architecture supports the mobility of the patients as well as the diversity of the medical cases. The features of the individual modules are discussed and the interconnection between the different underlying modules and tiers is explained. Task scheduling and allocation approach is proposed to effectively balance healthcare tasks distribution. The performance evaluation of the proposed approach is presented with different number of tasks and cloud nodes. The simulation results show acceptable results in terms of miss ratio, cost, and latency.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it