Context-aware and location-based service discovery protocol for Vehicular Networks: Proof of correctness
Bibliographic record
Abstract
Recent advances in Intelligent Transportation Systems (ITS) and the growing number of applications in Vehicular Networks (VN) have attracted many researchers. Applications in Vehicular Networks comprise but are not limited to safety applications and convenience applications. Deploying Vehicular Networks widely cannot be accomplished without overcoming the existing research challenges in such networks. Service discovery is one of the main challenges in Vehicular Networks which are characterized by their large scale and high mobility. Existing service discovery techniques for ad hoc networks cannot be applied directly to Vehicular Networks scenarios due to their poor performance and decreased efficiency in Vehicular Networks. In this paper, we present our location-based service discovery protocol for Vehicular Networks (LocVSDP). Our protocol permits the discovery of location-aware and time-sensitive services in Vehicular Networks. Moreover, our LocVSDP protocol improves service discovery efficiency by integrating service information into the network layer and using diverse channels. In our protocol, we present our efficient mechanism that permits to service requesters to find service providers and their routing information simultaneously, which results in overall bandwidth savings. We make use of diverse channels for the exchange of discovery and routing packets, which decreases the congestion on single channels and improves the delay of service discovery. Then, we present our efficient location-based request propagation and an efficient computation of the service reply, based on the specified location of the requested service in the drivers request. We discuss the implementation of our protocol and present its proof of correctness.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".