Integer Programs for Contention Aware Connected Dominating Sets in Wireless Multi-Hop Networks
Bibliographic record
Abstract
Efficient propagation of data across mobile nodes is essential in wireless networks. A minimum connected dominating set (MCDS) of nodes is typically used to reduce redundant transmission in broadcasts. If a group of nodes wants to transmit over a shared channel simultaneously, then contention occurs. Contending nodes then defer transmissions for a random time. A contention aware connected dominating set (CACDS) that minimizes transmission conflict is therefore essential. We study integer programming formulations computationally for MCDS and CACDS. We use Benders decomposition to solve them and propose a new method to compute Bender’s feasibility cut based on the number of connected components.We evaluate the state-of-art approach computationally for MCDS and CACDS based on the shortest paths with our approach. The detailed experiments show that the new method takes less time and minimizes contention better in large networks.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".