Routing Algorithm Based on Multi-Community Evolutionary Game for VANET
Bibliographic record
Abstract
Vehicular Ad Hoc Network (VANET) is a special application of Mobile Ad Hoc Networks in road traffic, which can autonomously organize networks without infrastructure. VANET that consists of many community nodes is characterized by lack of guaranteed connectivity. The right operation of such a network requires nodes to cooperate on the level of packet forwarding. When a node wants to transmit a message to another node, the message can be opportunistically routed through relay nodes under the hypothesis that each node is willing to participate to forward. However, nodes belonging to different communities may choose selfish behavior when considering their limited resources such as energy, storage space and so on. Their purpose is maximizing their own payoff. Thus, a new routing algorithm specifying certain message forwarding strategies is a necessity in such networks. In this work, we study main properties of sparse VANET. We presents a routing algorithm based on the evolutionary game, Multi-Community Evolutionary Game Routing algorithm (MCEGR), to solve the selfish routing problem. The theoretical analysis and simulation results show that the proposed routing has better feasibility and effectiveness.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".