Non-Cooperative and Cooperative Caching Schemes for Vehicular Networks
Bibliographic record
Abstract
Proactive caching, as one of the key features offered by 5G networks, has recently received much interest.It takes advantage of the information available about individual users which may include the user's contexts of interest and temporal and spatial mobility patterns.Acquiring these patterns requires the network to track, learn and build mobility and demand profiles of individual users.Harnessing this information has proven to make tangible improvements in the quality of service (QoS) offered by emerging 5G VehicularAd Hoc Networks (VANETs).In this thesis, we propose novel proactive caching schemes for minimizing the communication latency in VANETs under freeway and city mobility models.The main philosophy that underlies these schemes is to exploit information that may be available a priori for vehicles' demands and mobility patterns.We consider two paradigms: cooperative, wherein multiple Roadside Units (RSUs) collaborate to expedite the transfer of information to the intended user, and non-cooperative, wherein each RSU operates independently of other RSUs in the network.To develop the proposed schemes, for each of the considered models we formulate optimization problems that expose the impact of contact time and demand profile of the vehicle on the optimal caching decision.Unfortunately, the developed formulations are NP-hard, and hence difficult to solve for moderate-tolarge problems.To circumvent this difficulty, we use the insight developed through the optimization framework to develop practical caching algorithms, which are shown
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".