Efficient location‐based topology control algorithms for wireless ad hoc and sensor networks
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Bibliographic record
Abstract
Abstract Topology control is an efficient strategy for improving the performance of wireless ad hoc and sensor networks by building network topologies with desirable features. In this process, location information of nodes can be used to improve the performance of a topology control algorithm and also ease its operations. Many location‐based topology control algorithms have been proposed. In this paper, we propose two location‐assisted grid‐based topology control (GBP) algorithms. The design objective of our algorithm is to effectively reduce the number of active nodes required to keep global network connectivity. In grid‐based topology control, a network is divided into equally spaced squares (called grids). We accordingly design cross‐sectional topology control algorithm and diagonal topology control algorithm based on different network parameter settings. The key idea is to build near‐minimal connected dominating set for the network at the grid level. Analytical and simulation results demonstrate that our designed algorithms outperform existing work. Furthermore, the diagonal algorithm outperforms the cross‐sectional algorithm. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it