SMART: A Secure Multi-Layer Credit Based Incentive Scheme for Delay-Tolerant Networks
Bibliographic record
Abstract
Delay-tolerant networks (DTNs) provide a promising solution to support wide-ranging applications in the regions where end-to-end network connectivity is not available. In DTNs, the intermediate nodes on a communication path are expected to store, carry, and forward the in-transit messages (or bundles) in an opportunistic way, which is called opportunistic data forwarding. Such a forwarding method depends on the hypothesis that each individual node is ready to forward packets for others. This assumption, however, might easily be violated due to the existence of selfish or even malicious nodes, which may be unwilling to waste their precious wireless resources to serve as bundle relays. To address this problem, we propose a secure multilayer credit-based incentive scheme to stimulate bundle forwarding cooperation among DTN nodes. The proposed scheme can be implemented in a fully distributed manner to thwart various attacks without relying on any tamperproof hardware. In addition, we introduce several efficiency optimization techniques to improve the overall efficiency by exploiting the unique characteristics of DTNs. Extensive simulations demonstrate the efficacy and efficiency of the proposed scheme.
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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 | 0.002 | 0.003 |
| 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) | 0.004 | 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".