Energy-Efficient Estimation of Correlated Data in Wireless Sensor Networks
Bibliographic record
Abstract
In this paper, we study the energy-efficient distributed estimation problem for a wireless sensor network where a physical phenomena that produces correlated data is sensed by a set of spatially distributed sensor nodes and the resulting noisy observations are transmitted to a fusion center via noise-corrupted channels. We assume a Gaussian network model where (i) the data being sensed at different sensors are correlated and the correlation structure (in the form of a correlation matrix) is known at the fusion center, (ii) the links between the local sensors and the fusion center are subject to multipath fading plus AWGN, and the fading gains are available to the receiver node, and (iii) the central node uses the squared error distortion metric. We first determine the optimum power-distortion regions assuming (i) a multiple-letter, and (ii) a single-letter square distortion characterization. Next, for the two distortion characterization, we investigate the performance of an uncoded transmission approach where the noisy observations are only amplified-and-forwarded to the fusion center. At the fusion center, two different estimators are considered: (i) minimum mean-square error estimator (MMSE) that exploits the correlation, and (ii) best linear unbiased estimator (BLUE) that does not require or exploit the knowledge of the correlation matrix. For both estimators, we solve for the optimal power allocation that results in a minimum total transmission power while satisfying some distortion level for the estimate (for both multiple-letter and single-letter distortion metrics). The numerical comparisons between the two schemes indicate that the MMSE requires less power to attain the same distortion provided by the BLUE. Furthermore, comparisons between power-distortion region achieved by the theoretically optimum system and the uncoded system indicates that the performance gap between the two system becomes small for low level of correlation between the sensor observations. If observations at all sensor nodes are uncorrelated, the uncoded system attains the theoretically optimum system performance.
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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 | 0.003 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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 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".