Optimum ConvergeCast Scheduling in Wireless Sensor Networks
Bibliographic record
Abstract
Target monitoring is an important ConvergeCast application of wireless sensor networks in which sensors monitor a set of targets, and forward the collected data using multi-hop routing to the same location, called the sink. Nearly all previously proposed models only output a set of link transmission configurations, i.e., sets of links that can simultaneously transmit, without providing an ordering of the transmission configurations, nor guaranteeing that such an ordering exists using only the prescribed number of slots. As such, they do not provide a valid schedule to achieve ConvergeCast, and only give a lower bound on the number of slots required for a schedule. In this paper, we propose a first one phase decomposition model and algorithm that outputs a complete and optimum scheduling, i.e., with the output consisting in an ordered sequence of transmission configurations that achieves ConvergeCast. In addition, we show that the resulting transmission graph is not necessarily a tree. The resulting algorithm provides much better schedules, up to 15% less time slots required, than those of the previous best available mathematical programming or heuristic approaches in the literature.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".