A Secure Resource Optimization Strategy Based on Utility Dominant in Vehicular Networks
Bibliographic record
Abstract
Prospectively, vehicular networks are envisioned to support vehicular-based, road-based, and traffic-based data sensing, transmitting and processing for intelligent transportation system applications, and eventually evolve towards a new paradigm, named vehicular networks (VNs), which bundle the characteristics of networks into vehicular networks. In VNs, since the conflict between resource utility and the quality of service (QoS), it remains an ongoing challenge about how to reasonably and effectively allocate resources that can meet QoS and fairness requirements at the same time which causes security problem because of the conflicts. To this end, we propose a utility-based dominant resource allocation optimization strategy in this paper to achieve security in VNs. We first establish a mapping model between user QoS requirements and resource demands, and then apply the improved dominant resource fairness scheme to obtain optimal allocation results. The effectiveness of this security strategy is proved theoretically through the constructed utility function and the mapping model. Experimental results demonstrate that our security strategy can not only maximize the ratio of provision over demand of users and the satisfied degree of services but also achieve the QoS and fairness requirements of users.
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 | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 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".