Data dissemination for delay tolerant vehicular networks
Bibliographic record
Abstract
We propose a message dissemination algorithm for location-aware services in vehicular networks. The objective is to reduce information delivery time in intermittently connected urban vehicular networks by using historical mobility information of vehicles. Roads are divided based on observed traffic density into dense and sparse paths and vehicles share their current knowledge about fastest possible message delivery time to contouring dense roads. We use a Dijkstra based shortest path calculation with link weights set to packet dissemination time based on observed traffic and available relays in the vicinity. To simplify the shortest path calculation we calculate delay under two strategies. The first strategy is to relay information to closest dense road and use relaying. The second strategy is to try carry and forward towards the destination. Historical mobility information is used to find the best carrier candidate. On dense roads, information will be broadcast towards the destination without the carrying phase, while on sparse roads knowledge about the historical mobility patterns improves the next relay selection efficiency. Using simulations we show the superiority of our proposed method in delay and reliability of packet delivery compared to conventional data dissemination methods such as VADD in city traffic scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".