Dynamic witness selection for trustworthy distributed cooperative sensing in cognitive radio networks
Bibliographic record
Abstract
Cooperative spectrum sensing by secondary user (SU) nodes in cognitive radio networks (CRNs) is a promising approach to increase the spectrum access efficiency and overall network performance. However, unreliable sensing results or malicious behaviors from cooperator SU nodes can be very disruptive and reduce the network performance. Trust and reputation modeling has been identified as one of the potential solutions to address this problem, but the current centralized trust evaluation approach in CRN lacks scalability. Although some decentralized trust models have been proposed in CRN, without proper protection mechanisms, they are vulnerable to collusive behaviors by the witness SU nodes when they share testimonies about the trustworthiness of neighboring SU nodes. In this paper, we propose a clustering based witness selection method to address this problem. By dividing the witness SU nodes testimonies about the trustworthiness of neighboring SU nodes into clusters, the proposed method helps SU nodes to select which witness's opinion to trust mode in the future. The proposed method has been studied using extensive computer simulation and has demonstrated good robustness against common collusive attacks.
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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 | 0.012 |
| 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 | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".