Energy‐efficient and localized lossy data aggregation in asynchronous sensor networks
Bibliographic record
Abstract
SUMMARY In wireless sensor networks, most data aggregation scheduling methods let all nodes aggregate data in every time instance. It is not energy efficient and practical because of link unreliability and data redundancy. This paper proposes a lossy data aggregation (LDA) scheme to reduce traffic and save energy. LDA selects partial child nodes to sample data at partial time slots and allows estimated aggregation at parent nodes or a root in a network. We firstly consider that all nodes sample data synchronously and find that the error between the real value of a physical parameter and that measured by LDA is bounded respectively with and without link unreliability. Detailed analysis is given on error bound when a confidence level is previously assigned to the root by a newly designed algorithm. Thus, each parent can determine the minimum number of child nodes needed to achieve its assigned confidence level. We then analyze a probability to bound the error with a confidence level previously assigned to the root when all nodes sample data asynchronously. An algorithm then is designed to implement our data aggregation under asynchronization. Finally, we implement our experiment on the basis of real test‐beds to prove that the scheme can save more energy than an existing algorithm for node selection, Distributive Online Greedy (DOG). Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.003 | 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 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".