Supporting Real-Time CBR Traffic in a Cognitive Radio Sensor Network
Bibliographic record
Abstract
In this paper we consider supporting real-time traffic in a cognitive radio sensor network (CRSN). A lot of traffic in wireless sensor networks (WSNs) requires strict quality of service (QoS) requirements, while most existing WSNs working in the license-free spectrum cannot provide guaranteed QoS. A cognitive radio network (CRN) can possibly support traffic with strict QoS requirements while avoiding high cost for accessing the licensed spectrum. The CRSN studied in this paper is cluster-based and supports both real-time traffic and best effort traffic. We consider two resource allocation policies in order to provide a higher priority to the real-time traffic. Mathematical models are developed to analyze the performance of the real-time traffic, and the analytical results are verified by computer simulations. Our results indicate that satisfactory real-time performance can be achieved in the CRSN.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".