Achieving Energy Efficient Transmission in Wireless Body Area Networks for the Physiological Monitoring of Military Soldiers
Bibliographic record
Abstract
In this paper, the energy consumption within a soldier worn wireless body area network (WBAN) is minimized by formulating and solving two optimization problems. In the first optimization problem, Karush-Kuhn-Tucker (KKT) optimality conditions are used to analytically determine the effect of jointly optimizing transmission power, payload size, and retransmissions on energy consumption for transmission along a link within a WBAN, under a packet error rate (PER) constraint. In the second optimization problem, sensor nodes with different source rates and placements on the body are configured to use variable transmission power, payload size, and retransmission in order to minimize the total energy consumption in the WBAN under a PER constraint. This is a non-linear non-convex problem that is converted into an easily solvable convex optimization problem using generalized geometric programming. The numerical results show that using the joint optimization approach for the transmission configuration parameters can increase the WBAN network lifetime by two folds when compared against a configuration that only optimizes transmission power and retransmissions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".