An empirical investigation of performance challenges within context‐aware content sharing for vehicular ad hoc networks
Bibliographic record
Abstract
Abstract Connected vehicles is a leading use‐case within the Industrial Internet of Things (IIoT), which is aimed at automating a range of driving tasks such as navigation, accident avoidance, content sharing, and auto‐driving. Such systems leverage vehicular ad hoc networks (VANETs) and include vehicle to vehicle and vehicle to roadside infrastructure communication along with remote systems such as traffic alerts and weather reports. However, the device endpoints in such networks are typically resource‐constrained and, therefore, leverage edge computing, wireless communications, and data analytics to improve the overall driving experience, influencing factors such as safety, reliability, comfort, response, and economic efficiency. Our focus in this article is to identify and highlight open challenges to achieve a secure and efficient convergence between the constrained IoT devices and the high‐performance capabilities offered by the clouds. Therein, we present a context‐aware content‐sharing scenario for VANETs and identify specific requirements for its achievement. We also conduct a comparative study of simulation software for edge computing paradigm to identify their strengths and weaknesses, especially within the context of VANETs. We use FogNetSim++ to simulate diverse settings within VANETs with respect to latency and data rate highlighting challenges and opportunities for future research.
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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 | 0.006 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| 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".