Routing on Mini-Gabriel graphs in Wireless Sensor Networks
Bibliographic record
Abstract
Routing and topology control for Wireless Sensor Network (WSN) are significantly important to achieve the following: 1) energy efficiency in resource constrained WSN and 2) High speed packet delivery. In this paper, we propose a framework for WSN which combines three design approaches: 1) clustering, 2) routing, and 3) topology control. In this framework, we implement an energy efficient zone-based topology and routing protocol. Afterwards, we propose for this framework a new set of graphs referred to as the Mini Gabriel (MG) graphs. The simulation results show that the framework based on the new set of graphs outperforms an existing geometric graph. This is in terms of the transmission energy consumptions of the network and the end-to-end data transmission delay. In addition, the proposed framework generally demonstrates the best performance in terms of the network energy consumption. Moreover, the MG demonstrates that it achieves the connectivity property. Achieving this property is critical for WSNs.
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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 | 0.001 | 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".