WSN16-4: Logical Topology Design and Interface Assignment for Multi-Channel Wireless Mesh Networks
Bibliographic record
Abstract
A multi-channel wireless mesh network (MC- WMN) consists of a number of stationary wireless routers, where each router is equipped with multiple network interface cards (NICs). Each interface operates on a distinct frequency channel. Two neighboring routers establish a logical link if each one has an interface operating on a common channel. Given the physical topology of the routers and other constraints, the logical topology formation algorithm determines the set of logical links. In general, since the number of NICs is limited, some logical links need to share an NIC in a router. The interface assignment algorithm determines the interface that a logical link should be attached to. In this paper, we formulate the logical topology design and interface assignment as a joint optimization problem to obtain an MC-WMN architecture, called TiMesh. We conducted extensive ns-2 simulation experiments to evaluate our algorithm and compared it with another MC-WMN architecture called Hyacinth. Simulation results show that our proposed scheme achieves a higher aggregated network goodput and lower end-to-end delay for both TCP and UDP traffic.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".