Fair Flow Control in Solar Powered WLAN Mesh Networks
Bibliographic record
Abstract
Wireless LAN mesh networks are used to provide Wi-Fi access for temporary events. In this type of application it is sometimes necessary to operate some of the mesh nodes using an energy sustainable source, such as solar power. When the network is deployed, each mesh node is equipped with a solar panel and battery combination which is sufficient to prevent network outage using an assumed traffic design profile. During post-deployment network operation however, the actual traffic flows may be different from that for which the nodes were originally provisioned. To prevent node outage, the network must flow control the inputs, and this should be done in as fair a manner as possible. In this paper we propose a mechanism for achieving fair flow control on a per-flow basis. We first formulate a bound which achieves the best max-min fair flow control subject to eliminating network outage. This bound is non-causal in that it uses knowledge of future solar insolation and traffic flows to determine the optimum flow control. The bound motivates a proposed causal flow control algorithm whose operation uses prediction based on access to on-line historical weather data. Our results show that the proposed algorithm eliminates node outage and performs very well compared to the optimum flow control bound for a variety of network scenarios.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".