Distributed Multichannel and Mobility-Aware Cluster-Based MAC Protocol for Vehicular Ad Hoc Networks
Bibliographic record
Abstract
Since vehicular safety applications require periodic dissemination of status and emergency messages, contention-based medium-access-control (MAC) protocols such as IEEE 802.11p have problems in predictability, fairness, low throughput, latency, and high collision rate, particularly in high-density networks. Therefore, a distributed multichannel and mobility-aware cluster-based MAC (DMMAC) protocol is proposed. Through channel scheduling and an adaptive learning mechanism integrated within the fuzzy-logic inference system (FIS), vehicles organize themselves into more stable and nonoverlapped clusters. Each cluster will use a different subchannel from its neighbors in a distributed manner to eliminate the hidden terminal problem. Increasing the system's 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 the proposed MAC protocol. The reliability and connectivity of DMMAC are analyzed in terms of the average cluster size, the communication range within the cluster and between cluster heads (CHs), and the lifetime of a path. Simulation results show that the proposed protocol can support traffic safety and increase vehicular ad hoc networks' (VANETs) efficiency, reliability, and stability of the cluster topology by increasing the CH's lifetime and the dwell time of its members.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".