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