Distributed energy‐efficient channel assignment in cognitive mesh network for IoT systems
Bibliographic record
Abstract
Abstract Internet of Things (IoT) has attracted a lot of attention to solve the real‐world problem to improve quality of life. Cognitive radio mesh networks (CRMNs) can provide connectivity to a large number of IoT nodes and have the potential to self‐organize and reconfigure to make efficient use of spectrum resources. The growing concern regarding energy consumption in communication infrastructure and low power IoT nodes has led researchers to regard minimizing of energy consumption. In this paper, we propose a distributed energy‐efficient channel assignment in CRMNs for the IoT systems. We formulate an optimization problem for channel assignment and sharing in CRMNs for IoT systems with an objective to maximize energy‐efficiency of the network. We consider constraints on both intra and inter‐link interference. We then propose two distributed approaches to solve the channel assignment problem: (1) greedy approach and (2) game theoretic approach while considering the scalability and self‐organization requirements of the CRMNs for the IoT systems. We then present extensive simulation results to demonstrate the performance of proposed schemes.
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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 | 0.001 |
| 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".