True ConvergeCast scheduling in Wireless Sensor Networks
Bibliographic record
Abstract
An important application of Wireless Sensor Networks (WSNs) is target monitoring: sensors monitor a set of targets, and forward the collected data using multi-hop routing to the same location, called the sink. The resulting communication pattern is called ConvergeCast. Several researchers studied the problem of assigning TDMA time slots in order to minimize data collection time using mathematical programming models, with the claim that those models provide an optimal or a near-optimal ConvergeCast schedule. However, those models only output a multi-set of so-called transmission configurations, i.e., sets of links that can simultaneously transmit, and which together cover the links in all paths from sensors to the sink. In particular, these models do not provide an ordering of the transmission configurations, nor do they guarantee that such an ordering exists using no more than the minimum number of slots their model outputs. In this paper, we give for the first time, a mathematical programming formulation for a complete and optimal solution, i.e., an ordered sequence of transmission configurations that achieves ConvergeCast. This solution provides much better results than heuristic approaches in the literature, and a “true” scheduling in comparison with the previous mathematical programming approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".