Review on Energy Efficient V2V Communication Techniques for a Dynamic and Congested Traffic Environment
Bibliographic record
Abstract
High Speed Electric vehicles are very significant in urban environment to accomplish the information exchange wirelessly. Meanwhile, energy crisis that the present generation is facing is contributed in major by wireless communication, specifically in (V2V) domain. As the traffic grows and the information content is quite bulky, V2V faces many challenges in terms of energy utilization and power wastage. Modern wireless architectures and allied researches need to be intact on V2V communication to minimize the power loss issues and maximizes the energy efficiency. This paper is focused on recent advance technologies in the field of V2V communication that aims in uninterrupted connectivity in a very dynamic and congested environment. A comparative study of different architectures and algorithms are provided in this paper, to enable the readers to select appropriate techniques based on the real-time traffic conditions. In addition, energy efficient beamforming algorithms and related optimization techniques are briefly described that contributes to a faithful information exchange between the high-speed vehicles.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".