The need for access point power saving in solar powered WLAN mesh networks
Bibliographic record
Abstract
Wireless LAN mesh networks are now being used to deploy Wi-Fi coverage in a wide variety of outdoor applications. In these types of networks, conventional WLAN mesh nodes must be operated using continuous electrical power connections. This requirement may often be very expensive, especially when the network includes expansive outdoor wireless coverage areas. An alternative is to operate some of the WLAN mesh nodes using an energy sustainable source such as solar or wind power. This eliminates the need for a fixed power connection, making the node truly tetherless and allowing more flexibility in node positioning. In this article we first review the background and recent activities in the area of energy sustainable WLAN mesh networks. These types of networks are provisioned geographically, in that the assigned resources are a function of the geographic region where the network is to be deployed. The theory behind this is briefly described using some sample North American locations. We then discuss the current shortcomings of IEEE 802.1 1 when used in these types of networks. IEEE 802.11 requires that the access point be continuously powered, and this requirement is a major barrier to deploying cost-effective sustainable energy networks in certain applications. Recent work is then reviewed that has begun to address the changes that would be required to the standard to better support these types of networks.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".