A Constant Factor Localized Algorithm for Computing Connected Dominating Sets in Wireless Sensor Networks
Bibliographic record
Abstract
Connected dominating sets (CDSs) are probably the most common way of constructing virtual backbones for broadcasting operation in wireless sensor networks. This is because such backbones guarantee to reduce unnecessary message transmissions or flooding in the network. In this paper we propose a simple localized algorithm to construct a small-sized CDS. Considering the sensors deployed in the plane, our main idea is based on the computation of convex hulls of sensor nodes (nodes are considered points in the plane) in a localized manner and a simple coloring scheme, which produces a CDS in unit disk graphs whose size is at most 38*|MCDS| where |MCDS| is the size of a minimum CDS. To the best of our knowledge, this is a significant improvement over the best published results in the same context [5]. We also analyze grids and trees to compute the exact approximation ratios for the problem. We show that our algorithm produces an optimal CDS if the graph is a tree and in the case of grids the approximation factor is 2.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".