Location-Aided Gateway Advertisement and Discovery Protocol for VANets
Bibliographic record
Abstract
Intelligent transportation systems (ITSs) are gaining momentum among researchers. ITS encompasses several technologies, including wireless communications, sensor networks, voice and data communication, real-time driving-assistant systems, etc. These state-of-the-art technologies are expected to pave the way for a plethora of vehicular network applications. However, ITS faces difficult issues when trying to widely deploy such networks and applications. The interconnection of different networks, even in the case of the Internet, is one of the main difficulties that is delaying the wide spread of vehicular networks. In this paper, we present a novel gateway discovery technique for vehicular ad hoc networks (VANets). Our protocol aims to provide an efficient hybrid adaptive Location-Aided Gateway Advertisement and Discovery (LAGAD) mechanism for VANets. First, it permits gateway clients to discover nearby gateways; then, gateways keep advertising themselves to their clients to permit client information about the route toward the discovered gateway without having to resort to reactive route discovery. We discuss the implementation of our algorithm and present its proof of correctness, in addition to the performance evaluation demonstrated through an extensive set of simulation experiments using a Manhattan mobility model. Our results indicate that our LAGAD scheme is scalable and that a significant success rate could be achieved using our algorithm while guaranteeing low response time (on the order of milliseconds) and low bandwidth usage when compared with other gateway discovery approaches. Moreover, our results indicate that LAGAD achieves a high delivery ratio of data packets, as well as a low end-to-end delay, and permits duplicate and ordered data packet reception at the destination gateway.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".