Vehicular Edge Computing: Architecture, Resource Management, Security, and Challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Vehicular Edge Computing (VEC), based on the Edge Computing motivation and fundamentals, is a promising technology supporting Intelligent Transport Systems services, smart city applications, and urban computing. VEC can provide and manage computational resources closer to vehicles and end-users, providing access to services at lower latency and meeting the minimum execution requirements for each service type. This survey describes VEC’s concepts and technologies; we also present an overview of existing VEC architectures, discussing them and exemplifying them through layered designs. Besides, we describe the underlying vehicular communication in supporting resource allocation mechanisms. With the intent to overview the risks, breaches, and measures in VEC, we review related security approaches and methods. Finally, we conclude this survey work with an overview and study of VEC’s main challenges. Unlike other surveys in which they are focused on content caching and data offloading, this work proposes a taxonomy based on the architectures in which VEC serves as the central element. VEC supports such architectures in capturing and disseminating data and resources to offer services aimed at a smart city through their aggregation and the allocation in a secure manner.
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.
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| 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 it