A novel reliability scheme employing multiple sink nodes for Wireless Body Area Networks
Why this work is in the frame
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Bibliographic record
Abstract
Wireless Body Area Networks (WBANs) offer tremendous benefits for remote health monitoring and real-time patient care. However, the success of such a system would primarily depend on the reliability of the data transmitted by the WBAN. Two aspects of data reliability need to be considered, namely, data accuracy and data freshness (or, recentness of data). Assuming that the sophistication of the sensors addresses the problem of data accuracy, the freshness of such data becomes crucial. The focus of this work is to ensure reduced delay of communication within the WBAN, thereby reducing the overall network delay, which in turn maintains data freshness at the end point. This paper presents a novel scheme that focuses on reducing packet losses, and hence, the associated delay, to maintain data transmitted by the WBAN as fresh as possible. The paper presents a scheme using multiple sink nodes to achieve the objective. The proposed scheme is validated through simulation analyses.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it