An energy-efficient transmission scheme for monitoring of combat soldier health in tactical mobile ad hoc networks
Bibliographic record
Abstract
In this paper, we propose an energy-efficient transmission scheme for monitoring soldier health in tactical mobile ad hoc networks (T-MANET). In the proposed scheme, a constrained route discovery algorithm is used to determine feasible routing paths between the source nodes and sink node. A cross-layer optimization approach is then used to determine the optimal routes and minimum power required to transmit data in the military UHF band such that the end-to-end packet delivery ratio (PDR) and end-to-end delay objectives are met. However, the optimal solution requires exponential complexity and is not suitable for implementation in resource constrained sensor motes. Therefore, we propose a heuristic algorithm called joint link node power allocation (JPA) that allocates power based on the presence of joint link nodes. From the analysis and numerical results, we find that JPA achieves energy consumption that is within 24% of the optimal value, but significantly reduces the solution complexity from exponential to polynomial by utilizing 6 times fewer iterations than the optimal solution to converge to a minimum energy solution.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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 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".