A cooperative game-theory model for bandwidth allocation in multi-hop wireless networks
Bibliographic record
Abstract
The use of multi-hop wireless networks is extensive and growing. Such networks often have no single administrative authority. As such, they are best modeled by game theory. Existing approaches to this problem have focused on competitive game-theory analysis. In this paper we study the problem from the perspective of cooperative game theory. We assume that nodes wish to maximize their individual utility and cooperate solely for the purpose of increasing that utility. By assuming that the utility of a node is equal to its bandwidth, and by adopting the KS-Raiffa solution to cooperation, we are able to compute the fair bandwidth allocation in two-hop wireless networks. We then generalize these results to arbitrary multi-hop wireless networks. By assuming that any node can perform denial-of-service attack on the network, we are able to prove that Raiffa cooperation will result in temporal fairness in the network. Our results suggest that the ideal MAC for a wireless network will be one based on KS-Raiffa solution.
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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 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".