A Service Discovery protocol for vehicular ad hoc networks: A proof of correctness
Bibliographic record
Abstract
Recently, vehicle networks are gaining great deal of attention from the research community. In order to provide efficient and pervasive road communication, Next Generation Vehicular Networks (NVN) are considered a promising solution. NVNs have unique characteristics and face challenging problems. Consequently, it is hard to use the traditional mechanisms and protocols in this type of network. Service discovery is a very challenging problem for NVN-based applications. Furthermore, to the best of our knowledge, very little work has been done to deal with the service discovery problem in NVNs. Due to the high mobility and density of vehicles, traditional discovery techniques do not perform well. To solve this problem, we propose a novel class of service discovery protocol that would allow vehicles to discover services through the vehicular 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, thus enabling efficient discovery characterized by low overhead and a high success rate. In this paper, we present the proof of correctness and the message and time complexities computation of our protocol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".