Adaptive incentive-compatible routing in VANETs for highway applications
Bibliographic record
Abstract
Vehicular Ad-hoc Networks (VANETs) experience diverse network environments ranging from infrastructure based with dense connected vehicle communication in urban highways to infrastructurless with sparse connected vehicle communication in rural highways. Routing protocols in such challenging environments should be adaptive to work with or without a fixed infrastructure under a quickly varying connected vehicle density. They should also be incentive-compatible in order to guarantee that their adaptive routing decisions are followed by all participants. In this paper, we propose a novel routing protocol for highway applications that is both adaptive and incentive-compatible. It is adaptive by switching between: Vehicle-to-Infrastructure (V2I) routing, Vehicle-to-Vehicle (V2V) direct routing and V2V delay-tolerant routing. It is also incentive-compatible as shown by the resulting Nash Equilibrium using a game theoretic analysis. In this analysis, we show how incentives are administered by the fixed network operator through a credit-based exchange system. Cooperative relay vehicles, with high credit balances, are rewarded with extra bandwidth allocations whereas selfish relay vehicles, with low credit balances, are punished with less bandwidth allocations. The fixed network operator administers this credit-based exchange system in order to maintain acceptable user experience by complementing the congestion-prone V2I routing with properly-incentivized V2V direct routing and V2V delay-tolerant routing. Our protocol has been evaluated using our in-house built simulation environment with realistic channel and highway mobility models. Results demonstrate the efficacy of the proposed protocol in comparison to other routing protocols.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".