DTSG: Dynamic time-stable geocast routing in vehicular ad hoc networks
Bibliographic record
Abstract
Vehicular ad hoc networks (VANETs) have emerged as an area of interest for both industry and researches because they have become an essential part of intelligent transportation systems (ITSs). Many applications in VANET require sending a message to certain or all vehicles within a region, called geocast. Sometimes geocast requires that the message be kept alive within the region for a period of time. This time-stable geocast has a vital role in some ITS applications, particularly commercial applications. This study presents a novel time-stable geocast protocol that works well even in too sparse networks. Moreover, since commercial applications sometimes make it necessary to change the duration of the stable message within the region, the dynamic nature of a geocast protocol should allow this time to be extended, reduced, or canceled without any additional cost. Therefore, we call it a dynamic time-stable geocast, DTSG, protocol. It works in two phases (the pre-stable period and the stable period), and the simulation results show that it works well in its performance metrics (delivery ratio and network cost). In addition, these results validate the protocol prediction of its performance metrics. Moreover, with the informed time of zero, all the intended vehicles will be informed as soon as they enter the region. The fact that the protocol is independent of the networks' density, the vehicles' speed, and the vehicles' broadcasting range, makes it more robust than others that fail in sparse networks or in high-speed nodes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".