Multihop V2I Communications: A Feasibility Study, Modeling, and Performance Analysis
Bibliographic record
Abstract
In typical wireless networks, multihop communication is a method used to establish connectivity between distant nodes. Adapting this technique to vehicular networks requires bypassing several challenging constraints imposed by the nature of vehicular environments (e.g., high mobility and speeds and repetitive link disruptions). This paper revolves around establishing a multihop connectivity path between an isolated source vehicle S and a faraway gateway roadside unit (RSU) D through cooperative vehicles serving as intermediate relays. A stochastic model is formulated for the purpose of deriving an expression for the probability of the existence of a connectivity path between S and D. Then, the dynamic changes in the network topology are carefully examined to present a tight upper bound for the average end-to-end packet delivery delay. Finally, taking into account the inherent contention-based nature of the employed IEEE 802.11p medium access control (MAC) protocol, together with several other limiting factors such as relay unavailability and hidden terminals, the per-hop and the end-to-end throughput expressions are presented. Extensive simulations are conducted for the purpose of validating the proposed model and examining the system's performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".