Performance Evaluation of Location-Based Service Discovery Protocols for Vehicular Networks
Bibliographic record
Abstract
A considerable number of Vehicular Network (VN) applications have been developed recently. These applications range from security and safety to traffic information and service location. However, several research challenges remain open concerning efficient service discovery in large scale VNs. Most of the existing service discovery strategies present high overhead and poor performance when applied to VNs. Furthermore, existing context-aware and location-based service discovery protocols are either designed without considering the particularities of VNs or are not scalable with the increase of network density and number of requests. In this paper, we present novel context-aware and location-based service discovery protocols (Election-Based LocVSDP and Naive LocVSDP) that offer a scalable framework for the discovery of time-sensitive and location based services in large scale VNs. We conduct a concrete set of simulation experiments to evaluate the performance of our techniques and compare the results with an existing location-based discovery protocol (VITP). Simulation results indicate that our techniques outperform the VITP protocol in terms of success rate, average response time and bandwidth usage. In essence, both LocVSDP protocols show a gain of 20 percent in terms of success rate, use at least 90 percent less bandwidth than VITP and their average response time is at least 10 percent lower than VITP for successful query transactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".