A secure routing algorithm based on nodes behavior during spectrum sensing in cognitive radio networks
Bibliographic record
Abstract
Routing in cognitive radio networks (CRNs) faces many limitations that make it challenging. First, traditional routing protocols cannot be directly applied in CRNs because they consider fixed frequency band. Second, cognitive radio enables dynamic spectrum access which causes adverse effects on network performance. Third, effective routing in CR Networks (CRNs) needs local and continual knowledge of its environment. Last, presence of malicious nodes and their misbehaving activities affect the route establishment and therefore reduce the network performance. In this paper, we address such limitations by combining spectrum sensing and routing to propose a novel routing algorithm that uses nodes' behavior during spectrum sensing phase as a routing metric. Through the spectrum sensing phase, nodes behavior is measured through a parameter called belief level (BL), which describes the node's reliability to correctly sense the spectrum and to use spectrum channels accordingly. Moreover, we secure the routing requests and reply messages by encrypting them utilizing the existing cryptography techniques. The proposed approach is designed to maximize security level of paths, minimize the effects of licensed users activity over spectrum channels, and reduce the total channels cost over the best path(s). Evaluation of the proposed approach shows that its performance outperforms many current state-of-the-art routing algorithms used in CRNs in terms of end-to-end delay, packet delivery ratio, and packet loss ratio.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".