Probabilistic Cooperative Caching in VANETs for Social Networking
Bibliographic record
Abstract
Social media traffic constitutes the highest percentage of Internet traffic, which is mostly facilitated by mobile devices. This leads to high cellular costs incurred by mobile users. To reduce these costs, we strive to enable social media users to rely more on vehicular rather than cellular networks for content access. However, this can be hindered by the high delay and low packet delivery ratio often associated with accessing data from distant content providers in vehicular networks. Thus, to bring the data closer to the requester, we propose the Probabilistic Cooperative Caching at Moving and Parked Vehicles (PCCMPV) scheme. In PCCMPV, we exploit the static and mobile nature of parked and moving vehicles, respectively, to dynamically populate valuable road segments with diverse cached data. To do so, we dynamically assign a probability of caching to nodes along the data delivery path to assess their importance as caching nodes. For parked vehicles, such a probability relies primarily on the traffic density of the corresponding road segment, as well as its closeness centrality, and remoteness from the nearest data holder. PCCMPV provides an implicit form of off-path caching by assessing the trajectory of moving vehicles encountered along the data delivery path to calculate their probability of caching. Performance evaluation of PCCMPV demonstrates significant improvements in terms of delay, packet delivery ratio, and cache hit ratio compared to other caching schemes in vehicular networks.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".