Predictive model for battery life in IoT networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The internet of things (IoT) is prominently used in the present world. Although it has vast potential in several applications, it has several challenges in the real‐world. One of the most important challenges is conservation of battery life in devices used throughout IoT networks. Since many IoT devices are not rechargeable, several steps to conserve the battery life of an IoT network can be taken using the early prediction of battery life. In this study, a machine learning based model implementing a random forest regression algorithm is used to predict the battery life of IoT devices. The proposed model is experimented on ‘Beach Water Quality – Automated Sensors’ data set generated from sensors in an IoT network from the city of Chicago, USA. Several pre‐processing techniques like normalisation, transformation and dimensionality reduction are used in this model. The proposed model achieved a 97% predictive accuracy. The results obtained proved that the proposed model performs better than other state‐of‐art regression algorithms in preserving the battery life of IoT devices.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it