FESCIM: Fair, Efficient, and Secure Cooperation Incentive Mechanism for Multihop Cellular Networks
Bibliographic record
Abstract
In multihop cellular networks, the mobile nodes usually relay others' packets for enhancing the network performance and deployment. However, selfish nodes usually do not cooperate but make use of the cooperative nodes to relay their packets, which has a negative effect on the network fairness and performance. In this paper, we propose a fair and efficient incentive mechanism to stimulate the node cooperation. Our mechanism applies a fair charging policy by charging the source and destination nodes when both of them benefit from the communication. To implement this charging policy efficiently, hashing operations are used in the ACK packets to reduce the number of public-key-cryptography operations. Moreover, reducing the overhead of the payment checks is essential for the efficient implementation of the incentive mechanism due to the large number of payment transactions. Instead of generating a check per message, a small-size check can be generated per route, and a check submission scheme is proposed to reduce the number of submitted checks and protect against collusion attacks. Extensive analysis and simulations demonstrate that our mechanism can secure the payment and significantly reduce the checks' overhead, and the fair charging policy can be implemented almost computationally free by using hashing operations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".