Source-Based Routing in Wireless Mesh Networks
Bibliographic record
Abstract
Wireless mesh networks (WMNs) are currently used to provide broadband access to the Internet anytime and anywhere. Generally, WMNs consist of mesh routers equipped with one or more interfaces allowing connectivity to the Internet through gateways. To route traffic from sources to destinations, many routing protocols have been proposed in the literature. However, most of them take into account at most one metric (e.g., interferences, packet losses, and load at gateways). Moreover, almost all of these schemes consider only one type of interferences: interflow or intraflow. In this paper, we propose a new source routing and gateway selection scheme, which is called source-based routing (SBR), that improves the performance of WMNs. SBR uses a novel routing metric, which is a combination of packet losses, intraflow and interflow interferences, and load at gateways, to select best paths to reach selected gateways. Simulation results show that the proposed SBR improves the network performance and outperforms existing routing schemes, which are based on expected transmission count (ETX), nearest gateway (NG; i.e., shortest path to gateway), load at gateways (LG), or interference ratio (IR); more specifically, SBR yields 33%, 26%, 13%, and 10% more throughput compared with LG, ETX, NG, and IR, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".