Context-aware and location-based service discovery protocol for vehicular networks
Bibliographic record
Abstract
Vehicular Networks (VN) have attracted recent attention from researchers mainly motivated by the potential applications that will leverage Intelligent Transportation Systems (ITS). Such applications include road safety and security, traffic monitoring and driving comfort. However, several research challenges must be overcome before Vehicular Networks can be wide deployed. One of these challenges comprises how vehicles and service providers could discover each other in a Vehicular Networks, which is well-known for its large scale and high-mobility nature. Therefore, existing service discovery techniques for low-mobility or wired networks cannot be applied directly to Vehicular Networks. Most service discovery strategies available present high overhead and poor performance in a Vehicular Network scenario. In this paper, we propose a context-aware and location-based service discovery protocol for next generation Vehicular Networks (LocVSDP). Our protocol offers a scalable framework for the discovery of time-sensitive and location based services in Vehicular Networks. Furthermore, LocVSDP is integrated into the network layer and uses channel diversity for improved service discovery efficiency. We discuss the implementation of our protocol and compare the message and time complexities of our protocol with the existing location-based service discovery protocol VITP. Our results indicate that our techniques outperform the VITP protocol in terms of message and time complexities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".