Solar Powered WLAN Mesh Network Provisioning for Temporary Deployments
Bibliographic record
Abstract
WLAN mesh networks are often installed to provide wireless coverage for temporary events. In many of these cases, the WLAN mesh nodes can be operated using an energy sustainable source such as solar power. Node resource assignment consists of provisioning each node with a solar panel and battery combination that is sufficient to prevent node outage for the duration of the deployment. In this paper we consider this resource assignment problem with the objective of minimizing the total battery cost for a given energy source assignment. A methodology and algorithms for determining this resource assignment are first given. We then study the problem in the presence of shortest path and energy aware routing. To evaluate the quality of the resource assignments, we develop a linear programming formulation which gives lower bounds on the network resource assignment. Competitive ratios for different routing algorithms are then used, which demonstrates their effectiveness. We also include the case where some of the deployed nodes are designated in advance as having a continuous power source. Our results show the resource savings which are possible using the design algorithms and the potential resource assignment benefits of energy aware routing.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".