ML-RASPF: A Machine Learning-Based Rate-Adaptive Framework for Dynamic Resource Allocation in Smart Healthcare IoT
Bibliographic record
Abstract
The growing adoption of the Internet of Things (IoT) in healthcare has led to an extensive growth in real-time data from wearable devices, medical sensors, and patient monitoring systems. This data and latency-sensitive environment poses a significant challenge to conventional cloud-centric infrastructures, which struggle with unpredictable service demands, link congestion, and end-to-end delay across distributed environments. Specifically, traditional cloud infrastructures struggle to deliver consistent service quality in smart healthcare, where both low end-to-end latency and adaptive service delivery rates are critical for life-saving applications. We propose ML-RASPF, a machine learning-based service delivery framework for efficient and scalable IoT service delivery in smart healthcare systems to address these issues. ML-RASPF formulates the provisioning task as a joint optimization problem that aims to minimize service latency and maximize delivery rate stability. The framework comprises three key components: (i) a network parameter initialization module, (ii) a supervised learning model for traffic demand prediction, and (iii) a reinforcement learning-based service adaptation engine for real-time resource allocation. These modules intelligently distribute workloads across a hybrid cloud environment. We evaluate ML-RASPF using a realistic smart hospital scenario involving IoT-enabled kiosks and wearable devices delivering both latency-sensitive and latency-tolerant services. Experimental results demonstrate that ML-RASPF significantly outperforms state-of-the-art edge–cloud systems in terms of latency reduction, energy efficiency, bandwidth utilization, and service delivery rate.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".