Virtual backbone based on MCDS for topology control in wireless ad hoc networks
Bibliographic record
Abstract
This paper proposes to utilize virtual backbone to handle control messages in ad hoc networks. The virtual backbone is built by using the Minimum Connected Dominating Set (MCDS) on a graph. The first part of this paper presents a new algorithm to construct the MCDS. The construction of the MCDS is formulated using the linear programming approach. We compared the performance of this procedure with those other previous approaches, and we find that our approach is less complex and gives the nearest solution to the optimal one. The second part of this paper presents different techniques of diffusion in ad hoc networks such as flooding, clustering, MP relay, and backbone based on MCDS, etc. The flooding technique is simple and efficient, but it is expensive in term of bandwidth, and causes excessive flows of message etc. Simulation results show that the approach of virtual backbone based MCDS outperforms flooding and MP relay.
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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".