SATS: Secure Data-Forwarding Scheme for Delay-Tolerant Wireless Networks
Bibliographic record
Abstract
In this paper, we propose a secure data-forwarding scheme, called SATS, for delay-tolerant wireless networks. SATS uses credits (or micropayment) to stimulate the nodes' cooperation in relaying other nodes' messages and to enforce fairness. SATS also makes use of a trust system to assign a trust value for each node. A node's trust value is high when the node actively forwards others' messages. The highly trusted nodes are preferable in data forwarding to avoid the Black-Hole attackers that drop messages intentionally to degrade the message delivery rate. In this way, SATS can stimulate the nodes' cooperation not only to earn credits but also to maintain high trust values to increase their chances to participate in future data forwarding. Our security evaluation demonstrates that SATS can secure the payment and trust calculation. The performance evaluation demonstrates that SATS can significantly improve the message delivery rate due to avoiding the Black-Hole attackers in message forwarding and stimulating the nodes' cooperation.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".