Location-aided gateway advertisement and discovery protocol for VANets: Proof of correctness
Bibliographic record
Abstract
The Internet access from vehicular networks is gaining great interest from the research community. In fact, vehicles should be able to connect to the Internet and communicate with different networks through gateways. Guaranteeing safety on the roads is the main objective of vehicular networks. Safety applications need to collaborate with other types of services for efficient safety assurance. Consequently, many services would coexist with safety applications, including the gateway discovery service, and share a limited bandwidth. Any solution to the gateway discovery problem in vehicular ad hoc networks (VANets) is subject to this limitation. In this work, we present a novel gateway discovery technique for VANets. Our protocol aims to provide an efficient hybrid adaptive location-aided gateway advertisement and discovery mechanism for VANets (LAGAD). It has the following unique and novel characteristics for gateway discovery in VANets: (i) it is built on top of the network layer; (ii) it uses channel diversity; (iii) and it is based upon a location-aided adaptation of the advertisement zone of the gateway. Our proposed protocol benefits from the routing information to find the gateway service and the routing information to the gateway at the same time saving the overall bandwidth. It uses diverse channels to exchange discovery and routing packets decreasing the congestion on single channels and decreasing the delay of gateway discovery. Our proposed gateway discovery protocol adapts the advertisement zone of gateways based on the location information and the velocity of requesting vehicles. We discuss the implementation of our algorithm, then present its proof of correctness and message and time complexities computations.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".