A game theoretic approach for resource allocation in Cognitive Wireless Sensor Networks
Bibliographic record
Abstract
Game theoretic adaptive algorithms can be successfully applied for distributed intelligent Cognitive Wireless Sensor Networks (CWSNs). The use of these algorithms avoids weaknesses of centralized CWSNs. In this paper, a noncooperative spectrum sharing game theoretic approach for CWSNs is proposed to determine the optimum spectrum demand. The main objectives of this approach are improving the flexibility, efficiency and fairness in spectrum allocation with guaranteed sum data rates. We introduce a rewarding scheme in this approach to promote the communication of the sensor nodes that have good channel qualities and residual power levels. Then we analyze the existence and the uniqueness of the Nash Equilibrium. The simulation results show that the energy efficiency of our method is higher than the traditional uniform Time Division Multiplexing (TDMA - Uniform) approach and traditional TDMA approach that depends on channel (TDMA - Channel Based).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".