Randomized Single-Path Flow Routing on SDN-Aware Wi-Fi Mesh Networks
Bibliographic record
Abstract
Wi-Fi Mesh Networks (WMNs) as a popular platform can be used for the construction of dynamic backhaul networks especially over small cells, which is an indispensable part of the 5G technology. Finding the optimal single-path flow routing solution over the multi-hop backhaul networks is a classic NP-hard problem and there are non-trivial drawbacks such as packet re-ordering to implement multi-path routing as a practical solution. However, Software Defined Networking (SDN) as an emerging paradigm can provide a great opportunity to implement fast and efficient solutions for routing the network flows over WMNs. In this paper, we propose a randomized single-path flow routing that can be applied to SDN-aware WMNs. The randomized nature of our introduced solution avoids the complexities of implementing a multi-path flow routing and it presents a viable routing scheme that guarantees certain performance bounds. In addition, it considers the key characteristics of wireless networks and it can be employed for multi-channel multi-radio WMNs. Through numerical results, we have shown that our solution follows the theoretical, tighter and more general performance bounds. Moreover, in contrast to most of the prior studies, the performance of the proposed solution is not only evaluated through a real testbed (in terms of the aggregated throughput and protocol overhead) but also compared with some of the most popular WMN routing protocols.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".