Delay Tolerant and Predictive Data Dissemination Protocol (DTP-DDP) for urban and highway vehicular ad hoc networks (VANETs)
Bibliographic record
Abstract
Vehicular Ad hoc Networks (VANETs) enable inter-vehicul-\newline ar data exchange that has a great potential to help resolving numerous issues on our roads, such as the dissemination of emergency information, traffic condition, infotainment data and other delay tolerant data. While disseminating data within a certain area of interest, the Flooding scheme provides the best delivery ratio, but it suffers from the well-known \textit{broadcast storm} problem. To this end, we propose a protocol that takes advantage of the Global Positioning System (GPS) with integrated maps. Using the data from a map together with its predictive mechanism, the data sender elects the further nodes that will rebroadcast the information. In addition, the protocol works in both urban and highway scenarios. However, it requires one-time snapshot of the one-hop vehicles, but there are no other beacon messages. Once it has the snapshot, the sender chooses the further rebroadcasting vehicle. A low signal handling mechanism was developed to handle the cases in which the reply-response messages cannot be delivered. A set of simulation experiments was conducted and results show that the proposed scheme alleviates the \textit{broadcast storm} problem while keeping delivery ratio on a par with the Flooding scheme by sacrificing some delay performance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".