An Energy-Efficient Dual Prediction Scheme Using LMS Filter and LSTM in Wireless Sensor Networks for Environment Monitoring
Bibliographic record
Abstract
Environmental monitoring is a practical application where a wireless sensor network (WSN) may be utilized effectively. However, the energy consumption issues have become a major concern in using a WSN, particularly in remote locations without readily accessible electrical power supply. In general, the activities of data transmission among sensor nodes and the gateway (GW) can be a significant fraction of the total energy consumption within a WSN. Hence, reducing the number and the duration of transmissions as much as possible while maintaining a high level of data accuracy can be an effective strategy for saving energy. To achieve this objective, a least mean square (LMS) filter is used for a dual prediction scheme (DPS), in this paper. The DPS is data quality-based, allowing both the sensor nodes and the GW to predict the data simultaneously. Only when the error between the predicted data and the real sensed data exceeds a predefined threshold, the sensor nodes will send the sensed data to the GW/another node and consequently will update the coefficients of the filter. It is observed that, with this scheme, the total number of transmissions and their overall duration can be effectively reduced, and therefore, further energy savings can be realized. With the developed methodologies, at least 62.3% of the total energy for data transmission could be saved while achieving a 93.1% prediction accuracy.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".