A Scalable Bandwidth-Efficient Hybrid Adaptive Service Discovery Protocol for Vehicular Networks with Infrastructure Support
Bibliographic record
Abstract
In recent years, we have witnessed a growing interest in Vehicular Networks from both the research community and industry. Several potential applications of Vehicular Networks are envisioned such as road safety and security, streaming services, traffic monitoring and driving comfort, just to mention a few. It is critical that the existence of convenience or driving comfort services do not negatively affect the performance of safety services. In essence, the dissemination of safety services or the discovery of convenience applications require the communication among service providers and service requesters through constrained bandwidth resources. Therefore, service discovery techniques for vehicular networks must efficiently use the available common resources. In this paper, we present a bandwidth-efficient and scalable hybrid adaptive service discovery protocol (VSDP) for Vehicular Networks. VSDP aims at providing high success ratio while guaranteeing low response time and good scalability when the number of requests increases. Our proposed protocol finds the service provider and its routing information simultaneously which results in overall bandwidth savings. It uses diverse channels to exchange discovery and routing packets, thereby decreasing the congestion on single channels and decreasing the delay of service discovery. Our proposed service discovery protocol adapts the advertisement zone size of service providers based on an efficient adaptation mechanism that takes into consideration network condition and application requirements. The adaptation process is based on an adjustment technique and a prediction technique. We discuss the implementation of our protocol, present its proof of correctness as well as the performance evaluation through an extensive set of simulation experiments and using different mobility models. Our results show the scalability of our protocol. They indicate that our techniques can achieve significant success rate (more than 90 percent), while guaranteeing low response time (in the order of milliseconds) and low bandwidth usage when compared to existing service discovery techniques.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".