An efficient QoS MAC for IEEE 802.11p over cognitive multichannel vehicular networks
Bibliographic record
Abstract
One of the most challenging issues facing vehicular networks lies in the design of an efficient MAC protocol adaptable to different traffic scenarios (urban and suburban environments). While each environment has its own characteristics leading to different challenges, the merit of this paper lies in developing a MAC protocol suitable for both. In this work, we propose an efficient Multichannel QoS Cognitive MAC solely dedicated for Vehicular Environments (MQOG). MQOG incorporates efficient channel negotiation on the dedicated control channel whereas data is transmitted on other channel without contention. Since vehicular environments are widely known to suffer from interference and multipath, MQOG assesses the quality of channel prior to transmission employing a dynamic channel allocation and negotiation algorithm to achieve significant increase in channel reliability and throughput. The channel assignment problem was solved in an integrated manner while taking into account the QoS of different frames(Safety and NonSafety. The uniqueness of this protocol lies in making use of the ISM Band and UNII-3 in case all available channels in DSRC were considered unreliable. The proposed protocol was implemented in OMNET++ 4.1 and extensive experiments demonstrated that the proposed MAC ensures the reception of safety messages much faster, more efficient and reliable than other existing VANet MAC Protocols.
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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 | 0.001 | 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 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".