Smart Data Transmission in IoT: AI Applications for Health and Air Quality Monitoring
Bibliographic record
Abstract
In today’s digital landscape, the Internet of Things (IoT) is playing an increasingly vital role in healthcare by enabling smart, connected applications that enhance patient monitoring, diagnostics, and overall well-being. However, the deployment of these IoT-based healthcare solutions presents several challenges, particularly regarding the efficient use of system resources such as data acquisition, storage, processing, and network bandwidth. Healthcare environments, where continuous and reliable data transmission is critical, generate vast amounts of medical and environmental data that must be transmitted through various communication technologies (Wi-Fi, Bluetooth, LTE, etc.). To address the issue of network congestion and resource constraints, we propose an intelligent data compression strategy tailored to healthcare-focused IoT systems. This approach optimizes data transmission by reducing the volume of data during the acquisition stage, while a prioritization mechanism ensures that the most critical health-related information is transmitted in real time. To validate our approach, we implemented it in an air quality monitoring system, focusing on pollutants with significant impacts on human health. The results demonstrate that our method effectively reduces network load while preserving the quality and relevance of transmitted healthcare data.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".