A game based routing algorithm for congestion control of multimedia transmission in VANETs
Bibliographic record
Abstract
When transmitting multimedia files in urban Vehicle Ad hoc Networks (VANETs), the routing protocol will find multiple next hops with different quality because of the intensive nodes. Owing to the selfishness of the nodes, all of the multimedia streams try to seize the high quality nodes, attempting to maximize their usage of the high quality nodes. Consequently, it would inevitably lead to network congestion, affecting the QoS performance of the network, and even cause network paralysis. To solve the problem, this work presents a shunting of multimedia game model and a game based routing algorithm for congestion control of multimedia transmission in VANETs. In this game model, each stream carrying messages is a player, and its strategy is to select the percentage of the steam routed through the high quality nodes. A payoff function based on node metrics is proposed. The existence and uniqueness of a Nash Equilibrium is proved. Finally, the results of simulation demonstrate the effectiveness of GRCCM.
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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 | 0.002 | 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".