Delay-Aware Optimization of Physical Layer Security in Multi-Hop Wireless Body Area Networks
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Bibliographic record
Abstract
Joint optimization of the physical layer security with end-to-end delay management is studied in the uniquely constrained context of wireless body area networks (WBANs). A game-theoretic framework is proposed wherein body-worn sensor devices interact in the presence of wiretappers and under fading channel conditions to find the most secure multi-hop path to the hub, while adhering to the end-to-end delay requirements imposed by the application. We model the problem as the search for a Nash network topology where no unilateral deviation in strategy by any single sensor node improves the secrecy of its transmissions, and provide a distributed algorithm guaranteed to converge to a Pareto-dominant Nash solution. The framework is evaluated through numerical simulations in conditions approximating actual deployment of WBANs for moving and stationary scenarios. Results validate the merits of the proposed framework to improve the security of transmissions compared with the star topology and IEEE 802.15.6 two-hop topology extension with a best-channel algorithm, at the expense of an admissible increase in the end-to-end delay.
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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 | 0.001 |
| 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