Partially Cooperative Scalable Spectrum Sensing in Cognitive Radio Networks Under SDF Attacks
Bibliographic record
Abstract
Massive Internet of Things (IoT) connectivity requires addressing spectrum congestion caused by spectrum scarcity in wireless communications. Over the past decade, cognitive radio (CR) has been proposed as a promising solution to utilize the licensed spectrum efficiently. Conventional spectrum-sensing approaches are complex and require statistical information about the behavior of licensed users, which is impractical. To overcome this limitation, several reinforcement-learning (RL)-based spectrum-sensing approaches have been proposed that are also highly adaptable to the dynamics of IoT environments. Additionally, cooperative-RL-based spectrum-sensing approaches have been widely used because they are more accurate than noncooperative approaches. However, the advantage comes at the cost of scalability due to increased information-sharing overhead. Furthermore, cooperative spectrum-sensing (CSS) approaches suffer from attacks on the network, such as sensing data falsification (SDF) attacks, which deteriorate sensing accuracy dramatically. In this article, we present a scalable, partially CSS algorithm that is highly resilient to SDF attacks. The novelty of the proposed algorithm lies in partial cooperation through coalition formation, which reduces sensing and information sharing overhead while improving sensing accuracy. Moreover, the algorithm learns to adapt the sensing participation percentage and selects the most rewarding channel for sensing to maximize rewards while minimizing energy consumption. The proposed algorithm outperforms state-of-the-art CSS algorithms in terms of sensing accuracy and overheard. Contrary to centralized CSS algorithms, the proposed algorithm’s performance is directly proportional to the number of devices; hence, it is suitable for massive connectivity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".