Modeling aggregation convergecast scheduling using constraints
Bibliographic record
Abstract
We consider the transmission scheduling problem for aggregation convergecast in wireless sensor networks. All transmissions required for a completed data collection to the sink node are to be scheduled in a single TDMA frame. Concurrent transmissions are possible within each TDMA slot as long as no collisions occur at the intended recipients of each transmission. We observe that previously proposed heuristics to solving the aggregation convergecast scheduling problem rely on a decomposition into two phases. The first phase is the construction of an aggregation tree, typically a Shortest Path Tree (SPT), and the second phase is scheduling transmissions. We express the constraints of the aggregation convergecast problem in a manner compatible with a constraint programming solver, and extract solutions for small sized instances. The, admittedly sparse, evidence suggests that the resultant aggregation trees is almost never SPTs and point to alternative considerations that would be useful when constructing aggregation convergecast scheduling heuristics.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".