A fuzzy-logic-based cluster head selection algorithm in VANETs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Due to vehicles high mobility, there have been many clustering-based MAC protocols proposed to control Vehicular Ad hoc Network topology more effectively. Cluster head (CH) selection and cluster formation is of paramount importance in a highly dynamic environment such as VANETs. In this paper, we propose a novel cluster head selection criteria where cluster heads are selected based on their relative speed and distance from vehicles within their neighborhood. The maintenance phase in the proposed algorithm is adaptable to drivers' behavior on the road and has a learning mechanism for predicting the future speed and position of all cluster members using fuzzy logic inference system. The simulation results show that the proposed algorithm has a high average cluster head lifetime and more stable cluster topology with less communication and coordination between cluster members compared to other schemes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 it