Optimal number and class selection of nodes in Wireless Mesh Networks
Bibliographic record
Abstract
In this work, the optimal network plan in terms of node class selection and number of nodes required to achieve a given network reliability in a wireless mesh network (WMN) was investigated. The network is said to be optimal if it minimizes the network implementation cost while achieving the application matrix requirements. In this work, these requirements are described in terms of communication range, minimum network reliability, and achievable throughput. A node class is generally characterized by its capabilities (such as transmitted power and sensitivity) and associated cost. In order to tackle this problem, we developed an analytical model and methodology for WMN optimization. The proposed model differs from previously used models in that it takes into account linkspsila dependencies of geometrically co-located nodes, the effect of boundary nodespsila connectivity, in addition to fading and shadowing effects. This methodology was used to find the structure of the optimal policy for two important topologies: the one-dimensional linear network topology, and the two-dimensional triangular lattice topology. The proposed methodology resulted in a qualitative and quantitative description of the optimal WMN planning policy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".