Sparsely‐deployed relay node assisted routing algorithm for vehicular ad hoc networks
Bibliographic record
Abstract
Abstract In this paper, we study the issue of routing in a vehicular ad hoc network with the assistance of sparsely deployed auxiliary relay nodes at some road intersections in a city. In such a network, vehicles keep moving, and relay nodes are static. The purpose of introducing auxiliary relay nodes is to reduce the end‐to‐end packet delivery delay. We propose a sparsely deployed relay node assisted routing (SRR) algorithm, which differs from existing routing protocols on how routing decisions are made at road intersections where static relay nodes are available such that relay nodes can temporarily buffer a data packet if the packet is expected to meet a vehicle leading to a better route with high probability in certain time than the current vehicles. We further calculate the joint probability for such a case to happen on the basis of the local vehicle traffic distribution and also the turning probability at an intersection. The detailed procedure of the protocol is presented. The SRR protocol is easy to implement and requires little extra routing information. Simulation results show that SRR can achieve high performance in terms of end‐to‐end packet delivery latency and delivery ratio when compared with existing protocols. Copyright © 2013 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".