Performance evaluation of a hybrid adaptive service discovery protocol for next generation Heterogeneous Vehicular Networks (HVNs)
Bibliographic record
Abstract
Recently, vehicle networks are gaining lots of interest from the research community. In order to provide efficient and pervasive road communication, Heterogeneous Vehicular Networks (HVN) are considered as a promising solution. HVNs have unique characteristics and face challenging problems. Consequently, it is hard to use the traditional mechanisms and protocols in this type of networks. service discovery is a very challenging problem for HVNs based applications. Furthermore, to the best of our knowledge, very little work has been done to deal with the service discovery problem in HVNs. Due to the heterogeneity of the vehicular network and the high mobility and density of vehicles, traditional discovery techniques do not perform well. To solve this problem, we first propose a new architecture for Next Generation Heterogeneous Vehicular Networks, then we propose a novel class of service discovery protocol that permits to vehicles to discover services through the heterogeneous wireless network. Our hybrid proposed technique combines both proactive and reactive discovery approaches. It is also adaptive because it adapts to the vehicular network conditions in order to permit efficient discovery in terms of low overhead, and high success rate. We present extensive simulation results to evaluate the performance of our scheme.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".