Context-Aware Support for Geographical Routing Protocols
Bibliographic record
Abstract
Efficient protocols for data packet delivery in Vehicular Ad-hoc NETworks (VANETs) are crucial to guarantee the correct forwarding of data. However, communication in VANETs is a challenging task not only for the high mobility of the vehicles, but as recent studies have shown, there is a negative impact on protocol performances caused by an obstacle such as another vehicle in the line-of-sight (LOS). Many different routing protocols were presented in the last years, but only few of them were considering the effect of non line-of-sight (NLOS) propagation. In this work, we present a new solution to improve the data packet delivery ratio considering the problems caused by a high mobility and NLOS condition. Our proposal can be integrated in every position-based routing protocol. In fact, our method creates a support context awareness of neighbors before letting the protocol choose the next hop, without interfering with the specific logic. Simulation results of our context-aware version have been compared with the original protocols and with a modified version in accordance with the principle that every violation of LOS involves a discard of the packet: our solution always perform better in terms of packet delivery ratio and delay.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".