Cross-Layer Design for Prompt and Reliable Transmissions Over Body Area Networks
Bibliographic record
Abstract
In this paper, we propose a cross-layer design to make ambulatory health monitoring via body area networks (BAN) more reliable and robust. The proposed design builds on our centralized body area network access scheme (CBAS), a receiver-initiated medium access control (MAC) scheme that improves the visibility of a BAN in a coexistent environment, where diverse networks with various physical and MAC protocols share the radio spectrum. The design enhances CBAS by incorporating a network layer scheme that improves the packet delivery ratio (PDR), while minimizing the need for multihop cooperative transmissions; thus, packet delay is less compromised to achieve higher PDRs. The MAC layer provides the network layer with local information about the quality of on-body links to enable the BAN to identify the most reliable links in a distributed manner. Extensive experimental results are presented, which give insights on how the proposed cross-layer design improves PDR and packet delay. Results show the effectiveness of the proposed design which takes advantage of dynamic scheduling and multihop relays as warranted by the link conditions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".