Voronoi diagram and convex hull based geocasting and routing in wireless networks
Bibliographic record
Abstract
Abstract In this paper, we propose a general algorithm (based on an unified framework for both routing and geocasting problems), in which message is forwarded to exactly those neighbors which may be best choices for a possible position of destination (using the appropriate criterion). We then propose and discuss new VD‐GREEDY and CH‐MFR methods and define R‐DIR, modified version of existing directional methods. In VD‐GREEDY method, these neighbors are determined by intersecting the Voronoi diagram of neighbors with the circle (or rectangle) of possible positions of destination, while the portion of the convex hull of neighboring nodes is analogously used in the CH‐MFR method. Routing and geocasting algorithms differ only inside the circle/rectangle. The proposed methods may be also used for the destination search phase allowing the application of different routing schemes after the exact position of destination is discovered. VD‐GREEDY and CH‐MFR algorithms are loop free, and have smaller flooding rate (with similar success rate) compared to directional method. We proposed to use dominating set concept to reduce flooding ratio significantly, with a marginal impact on success rate and hop count. Simulations, involving the proposed and some known algorithms, are performed for two basic scenarios, one for geocasting and reactive routing, and the other for proactive routing, and both showed that our methods have higher success rate and lower flooding rate compared to existing methods. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".