A Cross-Layer Framework for Network Management in Wireless Sensor Networks Using Weighted Cognitive Maps
Why this work is in the frame
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Bibliographic record
Abstract
Achieving the end-to-end goals and objectives of Wireless Sensor Networks (WSN) is a highly challenging task. Such objectives include maximizing network lifetime, guaranteeing connectivity and coverage, and maximizing throughput. In addition, some of these goals are in conflict such as network lifetime and throughput. Cross-layer design can be efficient in proposing network management techniques that can consider different network objectives and conflicting constraints. This can be highly valuable in challenging applications where multiple Quality of Service (QoS) requirements may be demanded. In this paper, a novel cross-layer framework for network management is proposed that particularly targets WSN with challenging applications. The proposed framework is designed using the tool known as Weighted Cognitive Map (WCM). The inference properties of WCMs allow the system to consider multiple objectives and constraints while maintaining low complexity. Methods for achieving different objectives using WCMs are illustrated, as well as how system processes can operate coherently to achieve common end-to-end goals. Using extensive computer simulations, the proposed system is evaluated. The results show that it achieves good performance results in metrics of network lifetime, throughput, and Packet Loss Ratio (PLR).
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 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 it