An Enhanced Control Overhead Messages Reduction Algorithm in VANET
Bibliographic record
Abstract
The mobility in Vehicular Ad-Hoc NETworks (VANETs) causes high topology changes and in turn leads to excessive control overhead messages and frequent link communication failures. Traditionally, clustering techniques have been used as the main solution to improve the stability of the clustered topology and to minimize the routing overhead messages generated due to the routing protocols. In the clustering techniques, the network is divided into multiple clusters and selecting one of the Cluster Members (CMs) as a Cluster Head (CH). Still, a problem occurs when the CMs periodically forwards of CM HELLO (CMHELLO) messages to the CH. Also, when the CH periodically broadcasts an CH ADvertiSement (CHADS) messages to announce itself to the CMs. Hence, minimizing control overhead messages in any cluster environment is an essential goal to efficiently use the network resources. In this paper, we propose an Enhanced Control Overhead messages Reduction Algorithm (ECORA) with the goal to reduce the CHADS messages that broadcasted by the CHs. Finally, we evaluate the performance of our proposed algorithm by comparing with other similar contributions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".