Routing Algorithm Based on Multi-Community Evolutionary Game for VANET
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it