Clustering and OFDMA-based MAC protocol (COMAC) for vehicular ad hoc networks
Bibliographic record
Abstract
The IEEE community is working on the wireless access in vehicular environments as a main technology for vehicular ad hoc networks. The medium access control (MAC) protocol of this system known as IEEE 802.11p is based on the distributed coordination function (DCF) of the IEEE 802.11 and enhanced DCF of the IEEE 802.11e that have low performance especially in high-density networks with nodes of high mobility. In this paper, we propose a novel MAC protocol where nodes dynamically organize themselves into clusters. Cluster heads are elected based on their stability on the road with minimal overhead since all clustering information is embedded in control channel's safety messages. The proposed MAC protocol is adaptable to drivers' behavior on the road and has learning mechanism for predicting the future speed and position of all cluster members using the fuzzy logic inference system. By using OFDMA, each cluster will use a set of subcarriers that are different from the neighboring clusters to eliminate the hidden terminal problem. Increasing the system reliability, reducing the time delay for vehicular safety applications and efficiently clustering vehicles in highly dynamic and dense networks in a distributed manner are the main contributions of our proposed MAC protocol.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".