EWPS: Emergency Data Communication in the Internet of Medical Things
Bibliographic record
Abstract
In this article, we explore the remote monitoring of patients in an on-demand service that can be implemented using the Internet of Medical Things (IoMT). Network congestion and delay are important issues that must be resolved to handle emergencies in IoMT. Backpressure scheduling is a well-known scheme to resolve network congestion and improve network throughput for emergency data packets. Traditional backpressure scheduling is known to categorize each packet and schedules emergency packets to be forwarded faster than regular packets. However, in large-scale networks, these scheduling algorithms find unnecessarily long paths. This leads to high end-to-end delay and decreases the performance of guaranteed delivery. To resolve these issues, this article proposes an event-aware priority scheduling algorithm for data packets. This model follows a single priority queue model to manage all packets and emergency packets have been identified by the threshold values. Also, a separate communication path has been identified as well to reduce the waiting time for each category of the packet. Meanwhile, to reduce delay in packet communication, a delay-efficient data aggregation tree is constructed, which is combined with the priority queue model. Our in-depth simulation results of the proposed model prove the novel contribution to reducing delay in emergency packet delivery and avoiding network congestion. Moreover, the proposed model is also compared with the existing state-of-the-art models to show its ability to outperform similar methodologies.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".