Bibliographic record
Abstract
Vehicle-to-everything (V2X) services are attracting a lot of attention in the research and industry communities due to their applicability in the landscape of connected and autonomous vehicles. Such applications have stringent performance requirements in terms of complex data processing and low latency communications which are utilized to ensure road safety and improve road conditions. To address these challenges, the placement of V2X applications through leveraging of edge computing paradigm, that distributes the computing capabilities to access points in proximity to the vehicles, presents itself as a viable solution. However, the realistic implementation of the edge enabled V2X applications is hindered by the limited computational power provided at the edge and the nature of V2X applications that are composed of multiple independent V2X basic services. To address these challenges, this work targets the efficient placement of V2X basic services in a highway scenario subject to the delay constraints of V2X applications using them and the limited computational resources at the edge. To that end, this work formulates a binary integer linear programming model that minimizes the delay of V2X applications while satisfying the resource requirements of V2X basic services. To demonstrate the soundness of the approach, simulations with varying vehicle densities were conducted, and the results reported show that it can satisfy the delay requirements of V2X applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.007 | 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".