A novel multi-hop clustering scheme for vehicular ad-hoc networks
Bibliographic record
Abstract
Vast applications introduced by Vehicular Ad-Hoc Networks (VANETs), such as intelligent transportation, roadside advertisement, make VANETs become an important component of metropolitan area networks. In VANETs, mobile nodes are vehicles which are equipped with wireless antennas; and they can communicate with each others by wireless communication on ad-hoc mode or infrastructure mode. Compared with Mobile Ad-Hoc Networks, VANETs have some inherent characteristic, such as high speed, sufficient energy, etc. According to previous research, clustering vehicles into different groups can introduce many advantages for VANETs. However, because a VANET is a high dynamic scenario, it is hard to find a solution to divide vehicles into stable clusters. In this paper, a novel multi-hop clustering scheme is presented to establish stable vehicle groups. To construct multi-hop clusters, a new mobility metric is introduced to represent relative mobility between vehicles in multi-hop distance. Extensive simulation experiments are run using ns2 to demonstrate the performance of our clustering scheme. To test the clustering scheme under different scenarios, both the Manhattan mobility model and the freeway mobility model are used to generate the movement paths for vehicles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".