ZOOM: Scaling the mobility for fast opportunistic forwarding in vehicular networks
Bibliographic record
Abstract
Vehicular networks consist of highly mobile vehicles communications, where connectivity is intermittent. Due to the distributed and highly dynamic nature of vehicular network, to minimize the end-to-end delay and the network traffic at the same time in data forwarding is very hard. Heuristic algorithms utilizing either contact-level or social-level scale of vehicular mobility have only one-sided view of the network and therefore are not optimal. In this paper, by analyzing three large sets of Global Positioning System (GPS) trace of more than ten thousand public vehicles, we find that pairwise contacts have strong temporal correlation. Furthermore, the contact graph of vehicles presents complex structure when aggregating the underlying contacts. In understanding the impact of both levels of mobility to the data forwarding, we propose an innovative scheme, named ZOOM, for fast opportunistic forwarding in vehicular networks, which automatically choose the most appropriate mobility information when deciding next data-relays in order to minimize the end-to-end delay while reducing the network traffic. Extensive trace-driven simulations demonstrate the efficacy of ZOOM design. On average, ZOOM can improve 30% performance gain comparing to the state-of-art algorithms.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".