Delay-Minimized Edge Caching in Heterogeneous Vehicular Networks: A Matching-Based Approach
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
To enable ever-increasing vehicular applications, heterogeneous vehicular networks (HetVNets) are recently emerged to provide enhanced and cost-effective wireless network access. Meanwhile, edge caching is imperative to future vehicular content delivery to reduce the delivery delay and alleviate the unprecedented backhaul pressure. This work investigates content caching in HetVNets where Wi-Fi roadside units (RSUs), TV white space (TVWS) stations, and cellular base stations are considered to cache contents and provide content delivery. Particularly, to characterize the intermittent network connection provided by Wi-Fi RSUs and TVWS stations, we establish an on-off model with service interruptions to describe the content delivery process. Content coding then is leveraged to resist the impact of unstable network connections with optimized coding parameters. By jointly considering file characteristics and network conditions, we minimize the average delivery delay by optimizing the content placement, which is formulated as an integer linear programming (ILP) problem. Adopting the idea of student admission model, the ILP problem is then transformed into a many-to-one matching problem and solved by our proposed stable-matching-based caching scheme. Simulation results demonstrate that the proposed scheme can achieve near-optimal performances in terms of delivery delay and offloading ratio with low complexity.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 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 it