Control Overhead Reduction in Cluster-Based VANET Routing Protocol.
Bibliographic record
Abstract
Vehicular Ad-Hoc NETworks (VANETs) are unique form of Mobile Ad-Hoc NETworks (MANETs), where the nodes act as vehicles moving with relatively high mobility, and moving in a predefined routes. The mobility in VANETs causes high topology changes and in turn leads to excessive control overhead and frequent link communication failures. Traditionally, clustering techniques have been used as the main solution to reduce the control overhead messages in VANET, in which 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 control overhead messages increase due to periodically forwarding of CM HELLO (CMHELLO) messages between the CMs and the CH, and when the CH periodically broadcasts an CH advertisement (CHADS) messages to declare itself to the CMs. In this paper, we propose a Control Overhead Reduction Algorithm (CORA) which aims to reduce the control overhead messages in a clustered topology. Therefore, we develop a new mechanism for calculating the optimal period for updating or forwarding the CMHELLO messages between the CMs and the CH. Finally, we evaluate the performance of our proposed work by comparing with other recent researches that published in this field. Based on the simulation results, the CORA algorithm significantly reduces the CMHELLO messages, where it generates the minimum percentage of CMHELLO messages compared with other techniques proposed on this field.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".