Energy Provisioning in Solar-Powered Wireless Mesh Networks
Bibliographic record
Abstract
Solar-powered wireless mesh nodes must be provisioned with a solar panel and battery combination that is sufficient to prevent node outage. This is normally done using historical solar insolation data for the desired deployment location and based on a temporal bandwidth usage profile (BUP) for each deployed node. Unfortunately, conventional methodologies do not take into account the use of energy-aware routing, and therefore, the deployed system may be overprovisioned and unnecessarily expensive. In this paper, we consider this resource assignment problem with the objective of minimizing the network deployment cost for a given energy source assignment. We first propose a resource-provisioning algorithm based on the use of temporal shortest-path routing and taking into account the node energy flow for the target deployment time period. We then introduce a methodology that incorporates energy-aware routing into the resource-assignment procedure. A genetic algorithm (GA) has been developed for this purpose. Our results show the large cost savings that an energy-aware resource assignment can achieve when compared with that done using the conventional methodology. To evaluate the quality of the resource assignments, we also develop a linear programming formulation that gives a lower bound on the total network resource assignment. Our results show that significant resource savings are possible using the proposed 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".