Energy Management in Solar Powered WLAN Mesh Nodes Using Online Meteorological Data
Bibliographic record
Abstract
Solar powered WLAN mesh nodes are assigned a solar panel and battery size based on power consumption profiles. If future loading exceeds the design target, then a node may not be able to achieve the outage performance for which it was configured. To prevent this from happening, forced power saving can be used to reduce node power consumption to acceptable levels. However, forced power saving generates a deficit in offered capacity which should be minimized as much as possible. In this paper we first formulate this as a non-linear control problem. An efficient linear programming approximation is then defined and solved based on an offline optimization where future solar insolation is known in advance. This provides a bound on the performance of any real control algorithm. We show that the LP solution is accurate in that it comes very close to achieving a no-control capacity deficit lower bound. A control algorithm is then proposed whose operation uses dynamic access to publicly available on-line meteorological data. The proposed approach uses this on-line data but could also benefit from on-line weather forecasting. Our results show that the proposed algorithm minimizes node outage and performs favorably compared to the offline and no-control lower bounds.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".