Wireless sensor networks with pressure-based energy forecasting: A simulation study
Bibliographic record
Abstract
Wireless sensor networks can be used to collect data in support of a number of tasks such as ecosystem or structural monitoring. When deployed in remote areas with little or no communication and power infrastructure, these networks face a number of power and reliability challenges. To collect adequate amount of data over long periods, operation of such networks must be managed to properly ration energy available in suitable storage devices and in the environment. In particular, predictive energy management can extend the duration a node is operational during its deployment and thus increase the quality of the collected data. This contribution presents a simulation of a small wireless sensor network that employs computationally inexpensive, short-term solar energy forecasts based on atmospheric pressure measurements as an input to a Takagi-Sugeno fuzzy control system. The proposed system shows improvement over a similar control scheme which only relies on knowledge of previously collected energy. Further improvements can be gained through optimization of the developed energy management scheme or by using more accurate energy availability prediction techniques.
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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 | 0.000 | 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 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".