Maximizing the lifetime of wireless sensor networks through domatic partition
Bibliographic record
Abstract
Distributing sensing and data gathering tasks to a dominating set is an attractive choice in wireless sensors networks since it helps prolong network lifetime by engaging such a subset of nodes for these tasks and letting other nodes go into energy-efficient sleep mode. Because they are busy all the time for sensing, processsing, and transmitting data, nodes in the dominating set quickly run out of energy. One possible way to overcome this situation is to find a number of dominating sets among the nodes of the network and use them one by one iteratively. In this paper, we investigate the problem of finding the maximum number of disjoint dominating sets called the domatic partition problem in unit disk graphs. Although the domatic partition problem is NP-hard in general graphs, it is unknown whether the same is true for unit disk graphs. However, we present an algorithm towards solving this problem (approximately) together with experimental results and give a conjecture based on our results about the maximum number of disjoint dominating sets in unit disk graphs.
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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 | 0.001 | 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 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".