SFoG-RPI: A Secured QoS Aware and Load Balancing Framework for FoG Computing in Healthcare Paradigm
Bibliographic record
Abstract
Diabetes, characterized by persistently high blood glucose levels, has been identified as a hazardous health condition, potentially leading to severe complications such as heart attacks, strokes, and heart failure.This study introduces a fog-based remote health monitoring system designed to mitigate the devastating impacts of diabetes and hypoglycemia.This system persistently monitors health parameters including glucose levels, carbohydrate intake, physical activities, heart rate, and blood pressure.It additionally supports advanced services such as feature extraction, distributed local storage, and enhanced security.The traditional cloud-based architecture, while effective, often results in significant latency due to the processing of vast amounts of data.By bringing computing servers closer to users, Fog computing addresses this issue, reducing latency, and increasing security, resource accessibility, and on-demand scaling.In this context, the proposed system aims to minimize latency and network usage while addressing critical issues such as security, access control, and privacy.It employs lossy data compression at the gateway level to decrease network bandwidth and enhance efficiency.Furthermore, the system introduces a novel Load Balancing mechanism to distribute the load among fog nodes evenly.It utilizes lightweight cryptographic algorithms, efficient key exchange protocols, and digital signatures to ensure confidentiality, authentication, and user privacy.The performance of the proposed framework was evaluated in terms of average processing time, energy consumption management, computational resource distribution, latency, and network usage.When compared with other systems, the proposed framework demonstrated superior results, thus validating its effectiveness.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".