A Priority-Aware Truthful Mechanism for Supporting Multi-Class Delay-Sensitive Medical Packet Transmissions in E-Health Networks
Bibliographic record
Abstract
In this paper, the design of priority-aware truthful mechanisms for multi-class delay-sensitive medical packet transmissions in electronic health (e-health) networks is studied. Unlike most of existing works, we focus on beyond wireless body area network (beyond-WBAN) communications, and consider the absolutely prioritized transmission scheduling as the realization of medical-grade quality of service, i.e., more critical medical packets have to always be transmitted prior to the ones with less emergency. In our model, medical packets arrive randomly at each WBAN-gateway (which ordinarily stands for one patient), and their beyond-WBAN transmission requests are reported to the network regulator (i.e., the base station) with different packet priorities which reflect their medical importance. The base station then dynamically manages the beyond-WBAN transmission service by formulating a multi-class multi-server priority queueing system. Taking into account the potential strategic behaviors from smart gateways, we design a truthful mechanism which can guarantee that all gateways will honestly report the actual priorities of their medical packets, while at the same time incentivize the base station to provide channel usages for e-health services. Theoretical analyses and simulation results examine the desired properties of our proposed mechanism, and demonstrate its feasibility and superiority compared to counterparts.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 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".