Enhancing Energy Efficiency in Sensor Cloud Through Time Series Forecasting of Sensor Data
Bibliographic record
Abstract
In today's interconnected world, diverse sensor types are critical for powering various applications and services.The limited energy resources of these sensors present a significant challenge in managing sensor networks efficiently.To address this, we propose an energy-saving sensor cloud that utilizes a data prediction technique.Typically, a sensor node in a Wireless Sensor Network (WSN) gathers and transmits data to the cloud every 10 minutes, consuming substantial energy.In contrast, our proposed method requires sensor nodes to communicate with the cloud every 110 minutes, as the cloud system's forecasting method is capable of predicting ten steps ahead, thus reducing transmission frequency.We have applied Wavelet-based Forecasting (WBF), Auto-Regressive Integrated Moving Average (ARIMA), and a hybrid ARIMA-WBF for these predictions.The ARIMA model demonstrates superior performance compared to the other techniques when dealing with linear sensor data.Our method results in a power consumption that is approximately 90.9% lower than that of traditional methods within the sensor cloud, owing to reduced data transmission frequency.Additionally, our approach yields notably lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) in predictions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".