Joint Channel Access and Sampling Rate Control in Energy Harvesting Cognitive Radio Sensor Networks
Bibliographic record
Abstract
In this paper, we investigate the network utility maximization problem in energy harvesting cognitive radio sensor networks (CRSNs). Different from traditional sensor networks, sensor nodes in CRSNs are embedded cognitive radio modules, enabling them to dynamically access the licensed channels. Since the dynamic channel access is critical to guarantee the network capacity for CRSNs, existing solutions without considering the dynamic channel access cannot be directly applied into CRSNs. To this end, we aim at maximizing the network utility by jointly controlling the sampling rates and channel access of sensor nodes, under the energy consumption, channel capacity and interference constraints. With the consideration of fluctuated energy harvesting rates and channel switching costs, we formulate the network utility maximization as a mix-integer non-linear programming problem and solve it in an efficient and decoupled way by means of dual decomposition. A joint channel access and sampling rate control scheme, named JASC, is then proposed considering the real-time channel sensing results and energy harvesting rates. Extensive simulation results demonstrate that JASC can efficiently improve the network utility in CRSNs based on a realistic energy harvesting dataset.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".