An Adaptive Edge Computing Infrastructure for Internet of Medical Things Applications
Bibliographic record
Abstract
The integration of cloud computing (CC) and Internet of Things (IoT) technologies in the healthcare industry has significantly boosted the importance of real-time remote patient monitoring. The Internet of Medical Things (IoMT) systems facilitate the seamless transfer of health records to data centers, allowing medical professionals and caregivers to analyze, process, and access them. This data is often stored in cloud-based systems. Nevertheless, the transmission of data and execution of computations in a cloud environment may lead to delays and affect the efficiency of real-time healthcare services. In addition, the use of edge computing (EC) layers has become prevalent in performing local data processing and storage to reduce service response times for IoMT applications. The main objective of this article is to develop an adaptive EC infrastructure for IoMT systems, with a specific emphasis on maintaining optimal performance for real-time health services. It also designs a model to predict the server resources required to meet service level agreements (SLAs) regarding response time. Simulation results demonstrate that EC significantly improves service response time for real-time IoMT applications. The proposed model can accurately and efficiently predict the computing resources required for medical data services to achieve SLAs under varying workload conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".