Increasing Energy Efficiency in Sensor Networks: Blue Noise Sampling and\n Non-Convex Matrix Completion
Bibliographic record
Abstract
The energy cost of a sensor network is dominated by the data acquisition and\ncommunication cost of individual sensors. At each sampling instant it is\nunnecessary to sample and communicate the data at all sensors since the data is\nhighly redundant. We find that, if only a random subset of the sensors acquires\nand transmits the sample values, it is possible to estimate the sample values\nat all the sensors under certain realistic assumptions. Since only a subset of\nall the sensors is active at each sampling instant, the energy cost of the\nnetwork is reduced over time. When the sensor nodes are assumed to lie on a\nregular rectangular grid, the problem can be recast as a low-rank matrix\ncompletion problem. Current theoretical work on matrix completion relies on\npurely random sampling strategies and convex estimation algorithms. In this\nwork, we will empirically show that better reconstruction results are obtained\nwhen more sophisticated sampling schemes are used followed by non-convex matrix\ncompletion algorithms. We find that the proposed approach coupling blue-noise\nsampling with non-convex reconstruction algorithm, gives surprisingly good\nresults.\n
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".