Power Saving and Load Balancing for Solar WLAN
Bibliographic record
Abstract
The IEEE 802.11 standard has been widely adopted as a Wireless LAN (WLAN) technology. This widespread proliferation of the technology has lead to an increase in the number of users taking advantage of so-called "hot-spots" which leads to an increased demand on bandwidth provided by Access Points (APs) in the hot-spot. The logical solution is to deploy more overlapping access points in the same coverage area, thus increasing the capacity of the system by providing load balancing services. However, when a hot-spot is located in an outdoor environment, it becomes difficult to provide the AP with power which is traditionally carried over wired links thus causing the service provider to incur additional costs, not to mention the impossibility in some cases of delivering power to the AP. This problem can be overcome by using solar-panel powered APs which we will refer to as solar nodes (SNs). In this thesis we examine the load-balancing problem that arises when two or more SNs are co-located in the same coverage area. We propose and evaluate two algorithms for efficiently distributing the load among them (transferring stations (STAs) from SN to neighboring SNs) and increasing their lifetime by using power saving schemes that co-ordinate the wake/sleep patterns of the SNs based on traffic load. Finally, a Connection Admission Control (CAC) function is proposed that the SN should use in order to provide controlled access to services. We demonstrate through simulations that our proposals can significantly reduce the hardware requirements and cost of SNs and improve the service perceived by STAs in terms of transmission delay.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".