GeoMobCon: A Mobility-Contact-Aware Geocast Scheme for Urban VANETs
Bibliographic record
Abstract
Geocast, which consists of delivering messages to a specific location, has become an important technique with the accelerated development of the location-based services in mobile networks. Geocast in the automotive domain is of particular interest, enabling many promising applications, such as geographic advertising, location-based traffic alerts, etc. Different from the conventional geocast algorithms focusing on the distance-based approaches, in this paper, we propose a node mobility-aware and node contact-aware geocast algorithm (GeoMobCon) for urban vehicular ad hoc networks from the delay-tolerant network perspective to better deal with the high mobility and transient connectivity issues. Different levels and aspects of vehicle mobility information and self-maintained contact information are employed, making GeoMobCon simple, scalable, and communication and computation effective. Practical issues are well considered by introducing real-world trace analysis, trace-driven simulation, and efficient buffer management. Extensive performance comparisons with other protocols have been conducted to show the advantages of GeoMobCon.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".