FAIR: fee arbitrated incentive architecture in wireless ad hoc networks
Bibliographic record
Abstract
In ad hoc wireless networks, nodes communicate through a cooperative network in which other nodes function as relays. Since resources are frequently constrained, incentives must be provided to entice nodes to relay. The correct amount of incentive is essential to the efficient and optimal operation of the network. Excessive incentive results in widespread altruism, leading to diminished system lifetime. In contrast, insufficient incentive leads to selfish nodes that dramatically decrease network throughput. Nodes are assumed to be rational, and seek to maximize their own utilities. We use virtual credits as the incentive to stimulate cooperative behavior between nodes. The result of this research is fee arbitrated incentive architecture (FAIR), an application layer protocol that enhances fairness and collaboration in ad hoc wireless networks. FAIR utilizes autonomous feedback mechanisms to configure itself to the changing demands of the nodes, users, and network conditions. PriceSim, a network simulator implementing the FAIR protocol, was created to evaluate FAIR's performance.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| 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".