Stimulating Cooperation in Multi-hop Wireless Networks Using Cheating Detection System
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Bibliographic record
Abstract
In multi-hop wireless networks, the mobile nodes usually act as routers to relay packets generated from other nodes. However, selfish nodes do not cooperate but make use of the honest ones to relay their packets, which has negative effect on fairness, security, and performance of the network. In this paper, we propose a novel incentive mechanism to stimulate cooperation in multi-hop wireless networks. Fairness can be achieved by using credits to reward the cooperative nodes. The overhead can be significantly reduced by using a cheating detection system (CDS) to secure the payment. Extensive security analysis demonstrates that the CDS can identify the cheating nodes effectively under different cheating strategies. Simulation results show that the overhead of the proposed incentive mechanism is incomparable with the existing ones.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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