A Lightweight and Efficient Approach (LEA) for Hovering Information protocols
Bibliographic record
Abstract
Keeping an event alive within a bounded area for a certain amount of time in Vehicular Ad Hoc Networks (VANETs) is still an open issue in the literature. Existing approaches tackle this problem by using periodic beacon messages that can quickly saturate the network. In this paper, we propose a novel method called LEA: A Lightweight and Efficient Approach for Hovering Information protocols, which is unique in the literature. Basically, instead of using a periodic beacons, LEA can use any kind of data dissemination protocol and execute it periodically to inform the new vehicles that are arriving into the Hovering Zone about the event. We also propose an equation to calculate the repetition interval for the dissemination. An extensive set of simulation experiments were conducted and results show that LEA can outperform the existing beaconing approaches in terms of total number of transmitted packets while also sustaining a satisfactory relevant delivery ratio for the vehicles that will really forward the event. In addition, the use of a hovering optimized data dissemination protocol can improve even further the efficiency of this approach in terms of time needed for a vehicle to become informed once in enters the Hovering Zone.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".