Energy Aware Provisioning in Solar Powered WLAN Mesh Networks
Bibliographic record
Abstract
WLAN mesh networks are often installed to provide wireless coverage for temporary events. In these types of networks, the WLAN mesh nodes can sometimes be operated using an energy sustainable source such as solar power. Resource provisioning consists of pre-assigning each node with a solar panel and battery combination that is sufficient to prevent node outage for the duration of the deployment. This is done by assuming a temporal load profile for each node, which is then used to perform the assignment using historical solar insolation data for the desired deployment location. Unfortunately, this methodology cannot take into account the state dependencies which occur when the network uses energy aware routing, and therefore the system may be over-provisioned. In this paper we propose a methodology for WLAN mesh node resource assignment that incorporates energy aware routing into the assignment algorithm. The problem consists of determining a network-wide minimum cost resource assignment subject to satisfying the input load profile. A genetic algorithm (GA) has been developed for this purpose. Our results show the large resource savings that energy aware resource assignment can achieve when compared to that done using the conventional methodology. We also study the competitive ratio of both resource assignment schemes and show that for small traffic overloading, energy aware routing performs better than shortest path routing in networks which are provisioned using the proposed methodology.
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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 | 0.002 |
| 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 | 0.001 | 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".