Towards Fresh and Low-Latency Content Delivery in Vehicular Networks: An Edge Caching Aspect
Bibliographic record
Abstract
Mobile edge caching which exploits the similarity in content requests to reduce duplicated transmissions, is considered as an effective solution to address the challenge of increasing mobile traffic demand and constrained radio resources. Different from conventional networks, vehicular networks are highly dynamic, and thus the cached contents should update timely to guarantee the freshness of vehicle received information. However, content update also consumes radio resource and results in a tradeoff between content freshness and service latency, calling for the joint optimization of content update, delivery, and radio resource allocation. To address this issue, this work proposes a cache-assisted lazy update and delivery (CALUD) scheme to balance content freshness and service latency in vehicular networks. Firstly, the age of information (AoI) and service latency of vehicular-received contents are derived in closed form under the CALUD scheme. Then, the CALUD scheme is further optimized jointly with the radio resource allocation from the network aspect to meet the diversified service latency and AoI requirements of different applications. Extensive simulations are conducted to validate the theoretical analysis using the OMNET++ simulator. The results demonstrate that the proposed CALUD scheme can reduce the service latency to milliseconds while guaranteeing the required content freshness.
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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 | 0.004 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".