Enhancing Virtual Sensors to deal with Missing Values and Low Sampling Rates
Bibliographic record
Abstract
Nowadays there is an increasing interest from the edge computing and IoT community for virtual sensors due to their advantages of low cost, robustness, easy installation and multi-purpose use. Virtual sensors are software components capable of replacing physical sensors providing aggregations and higher representations of physical measurements. Since virtual sensors rely on taking input and process measurements from external sources of data, they bear their limitations. To this end, in this paper, we tackle the challenges of missing values and low sampling rate. Specifically, our research goal is to design a virtual sensor that operates smoothly even if it misses some input values. Additionally, even if the sampling rate of the external input is low, the virtual sensor will be capable of providing output values in a higher rate. In order to achieve these functionalities we examine and tailor different lightweight deep learning models appropriate for an edge computing setting. For the experimental evaluation we also developed an IoT platform and run a smart home use case with humidity and temperature sensors. Comparing the evaluation outcomes of our methodology with baseline missing values techniques and multi-step approaches, our proposed methodology is proved to be promising and accurate.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".