A Truthful Mechanism for Scheduling Delay-Constrained Wireless Transmissions in IoT-Based Healthcare Networks
Bibliographic record
Abstract
In this paper, the scheduling management of delay-constrained medical packet transmissions in Internet of Things (IoT)-based healthcare networks is studied. Unlike most existing works in the literature, we focus on beyond wireless body area network (beyond-WBAN) communications, i.e., data transmissions between smart WBAN-gateways (e.g., smartphones) and the base station (BS) of remote medical centers. In our model, various medical packets are randomly aggregated at each gateway (which ordinarily stands for one patient), and their delay-constrained beyond-WBAN transmission requests are immediately reported to the network controller (i.e., BS) with different priority levels reflecting their medical importance. The BS schedules the uplink beyond-WBAN transmissions by forming a queueing system which addresses specific medical-grade quality of service requirements, including the priority awareness and the delay constraints of medical packet transmissions. By taking into account the natural device intelligence of smart gateways in IoT-based networks, we design a truthful and efficient mechanism which can prevent gateways from strategically misreporting the priority levels of medical packets, while incentivizing the BS to manage the transmission scheduling according to the desired manner. Both theoretical and simulation results examine the feasibility of the proposed mechanism and demonstrate its superiority over the 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 | 0.016 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 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".