BOOST: A Connected Dominant Set-Aware Energy-Efficient Scheme for Software Defined Connected Autonomous Vehicular Networks
Bibliographic record
Abstract
In recent years, advancements in vehicular communication has improved road safety along with passenger convenience for many applications. However, to take intelligent and timely decisions, a large number of complex operations need to be get executed on large amount of data base repositories which in turn generates a huge burden on the underlying network infrastructure leading to a large amount of energy consumption. Most of the existing solutions reported for the aforementioned problems are based upon the traditional monolithic solutions which may not be applicable in modern scenarios in this environment. Hence, to mitigate the aforementioned challenges and constraints, in this article, we propose BOOST, a connected dominating set (CDS)-aware energy-efficient clustering scheme for Software Defined Network by integrating V2I and V2V communications for reliable and seamless data delivery. The proposed scheme has been specifically designed for urban scenario to achieve effective data delivery with minimum energy consumption. By leveraging the benefits of CDS on roadside communication infrastructure, BOOST is able to adapt with varying traffic conditions to provide seamless scalability with minimum energy and network overheads. The proposed scheme has been evaluated using various performance evaluation metrics in comparison to the existing benchmark schemes. The results obtained demonstrate its superior performance by 3% to 4% in terms of energy-efficiency, network overhead, packet delivery rate, and network throughput in comparison to the existing benchmark schemes.
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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 | 0.002 |
| 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".