Fine-grained access provisioning via joint gateway selection and flow routing on SDN-aware Wi-Fi mesh networks
Bibliographic record
Abstract
In recent years, dramatic growth of mobile data traffic has left the operators no choice but to consider Wi-Fi networks as an economic complementary solution. To achieve this, WLANs require to adopt some of the key features of carrier-grade operators, such as centralized resource management. As an emerging paradigm, Software Defined Networking (SDN) can be used to provide salient centralized network solutions for Wi-Fi infrastructures. In fact, applying SDN to different wireless platforms, e.g., Wi-Fi Mesh Networks (WMNs), brings unprecedented opportunities to improve the network performance by employing more sophisticated algorithms at SDN controllers. Moreover, it should be noted that traffic engineering over WMNs incorporates tightly correlated steps including association control, gateway selection and flow routing which are individually NP-hard problems. In this paper, we present an agile and fine-grained access provisioning solution via bridging the cellular and Wi-Fi technologies that empowers us to address the users demand by steering data flows on different tiers of WMNs. In contrast to the prior work, we present a detailed unified formulation for joint gateway selection and flow routing in Multi-Channel Multi-Radio (MCMR) WMNs that considers the key attributes of wireless networks. The functionality of the presented solution is evaluated through various experiments with extensive numerical results.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".