IEEE Access Special Section Editorial: Lightweight Security and Provenance for Internet of Health Things
Bibliographic record
Abstract
As an extension of the Internet of Things (IoT), the Internet of Health Things (IoHT) plays an important role in the remote exchange of data of different physical processes such as patient monitoring, treatment progress, observation, and consultation. In IoHT, connectivity, integration, computation, and interoperability are enabled through various sensors, actuators, and controllers, thereby providing seamless connectivity with efficient utilization of resources. With advances in telemedicine, telesurgery, and other healthcare applications, streaming has become an essential part of IoHT. The data traffic in IoHT applications, such as interactive multimedia streaming, real-time image processing, traffic generated from faulty sensors and vital signs, can tolerate packet loss but has stringent delay requirements. On the other hand, video streaming applications cannot tolerate jitter. Similarly, the low-power devices are sensitive to packet loss and the periodic physiological traffic of medical traffic can tolerate delay or jitter, but not packet loss. Routing data in different IoHT applications has varying quality-of-service (QoS) requirements in terms of delay, packet loss, jitter, and throughput.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.021 | 0.013 |
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".