A new stability based clustering algorithm (SBCA) for VANETs
Bibliographic record
Abstract
Lately, extensive research efforts have been dedicated to the design of clustering algorithms to organize nodes in Vehicular Ad Hoc Networks (VANETs) into sets of clusters. However, due to the dynamic nature of VANETS, nodes frequently joining or leaving clusters jeopardize the stability of the network. The impact of these perturbations becomes worse on network performance if these nodes are cluster heads. Therefore, cluster stability is the key to maintain a predictable performance and has to consider reducing the clustering overhead, the routing overhead and the data losses. In this paper, we propose a new stability-based clustering algorithm (SBCA), specifically designed for VANETs, which takes mobility, number of neighbors, and leadership (i.e., cluster head) duration into consideration in order to provide a more stable architecture. Extensive simulations show that the proposed scheme can significantly improve the stability of the network by extending the cluster head lifetime longer than other previous popular clustering algorithms do.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".