Transmission Management of Delay-Sensitive Medical Packets in Beyond Wireless Body Area Networks: A Queueing Game Approach
Bibliographic record
Abstract
In this paper, the management of delay-sensitive medical packet transmissions in beyond wireless body area networks (beyond-WBANs) is studied. The considered system addresses the random arrival of sensed medical packets at each WBAN-gateway, which are categorized into different classes (one class of emergent alarms and multiple classes of non-emergent routines). Upon receiving a medical packet, the associated gateway immediately declares a beyond-WBAN transmission request to the base station (BS). With the consideration of medical-grade quality of service (mQoS) requirements, the beyond-WBAN transmissions of heterogeneous packets are scheduled by following the constructed queueing models with specifically designed priority disciplines. By further considering the potential strategic behaviors of smart gateways, a non-cooperative delay-dependent prioritized queueing game for the beyond-WBAN transmission management is formulated. After that, we propose a novel analytical framework to jointly characterize the queueing performance and the properties of the game equilibrium. Theoretical and simulation results justify the feasibility and applicability of our designed transmission management system in beyond-WBANs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".