A Weighted Moving Average-based Approach for Cleaning Sensor Data
Bibliographic record
Abstract
Nowadays, wireless sensor networks have been widely used in many monitoring applications. Due to the low quality of sensors and random effects of the environments, however, it is well known that the collected sensor data are noisy. Therefore, it is very critical to clean the sensor data before using them to answer queries or conduct data analysis. Popular data cleaning approaches, such as the moving average, cannot meet the requirements of both energy efficiency and quick response time in many sensor related applications. In this paper, we propose a hybrid sensor data cleaning approach with confidence. Specifically, we propose a smart weighted moving average (WMA) algorithm that collects confidence data from sensors and computes the weighted moving average. The rationale behind the WMA algorithm is to draw more samples for a particular value that is of great importance to the moving average, and provide higher confidence weight for this value, such that this important value can be quickly reflected in the moving average. Based on our extensive simulation results, we demonstrate that, compared to the simple moving average (SMA), our WMA approach can effectively clean data and offer quick response time.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".