Energy-Efficient Compressive State Recovery From Sparsely Noisy Measurements
Bibliographic record
Abstract
The compressive sensing theory has been intensively inspired to develop new methods and applications. Energy conservation for every node and overall energy consumption in the network is one of the main design issues in such networks. In large-scale sensor networks, information is relatively sparse compared with the number of nodes. In such networks, the state recovery problem can be recast as a sparse signal recovery problem in the discrete spatial domain to be solved with a small number of linear measurements as an underdetermined linear system by anl1-norm minimization program. A series of signal processing methods is applied to recover the current state of sensory data, e.g., temperature, pressure, force, flow, humidity, position, or motion, from noisy measurements. We propose an energy-efficient state recovery method for sensor networks; then, the proposed method is employed to recover an air quality signal in an air-quality-monitoring system. The signal contains the air quality indexes for all monitoring sites in the system. To have a sparse representation of the signal, it is first transformed to frequency domain and then recovered and reconstructed from a small portion of coefficients. Different random matrices driven from Bernoulli and Gaussian distributions are investigated to find an energy-efficient sensing scheme for signal reconstruction. The results reveal more than 60% saving in power consumption with only 10% reconstruction and recovery error. The proposed method prolongs network lifetime with noticeable saving in deployment and maintenance cost, particularly in large-scale sensor networks with slowly varying phenomena.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".