PIS: A Practical Incentive System for Multihop Wireless Networks
Bibliographic record
Abstract
In multihop wireless networks (MWNs), the mobile nodes usually act as routers to relay other nodes' packets to enable new applications and enhance network performance and deployment. However, selfish nodes may not cooperate and make use of the cooperative nodes to relay their packets, which has a negative effect on network fairness, security, and performance. Incentive systems implement micropayment in the network to stimulate the selfish nodes to cooperate. However, micropayment schemes have originally been proposed for Web-based applications; therefore, a practical incentive system should consider the differences between Web-based applications and cooperation stimulation. In this paper, first, these differences are investigated, and a payment model is developed for the efficient implementation of micropayment in MWNs. Second, based on the developed payment model, an incentive system is proposed to stimulate the nodes' cooperation in MWNs. Third, a reactive receipt submission mechanism is proposed to reduce the number of submitted receipts and protect against collusion attacks. Extensive analysis and simulations demonstrate that our incentive system can secure the payment and reduce the overhead of storing, submitting, and processing payment receipts significantly, which can improve the system's practicality due to the high frequency of low-value payment transactions.
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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 | 0.003 | 0.005 |
| 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 | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".