Optimal placement of gateways in multi-hop Wireless Mesh Networks: A clustering-based approach
Bibliographic record
Abstract
Choosing strategic locations to optimally place gateways prior to network deployment in wireless mesh networks (WMNs) can alleviate a number of performance related problems; it can also lead to better handling of network scalability. Existing solutions that address the optimal gateway placement problem differ mainly in terms of the set of constraints that the placed gateways has to satisfy; the resulting placements influence, differently, the network quality of service (QoS). In this paper, we study the WMN topology design and we propose a clustering based gateway placement algorithm (CBGPA) that guarantees end-to-end bounded delay communications with a good handling of network scalability. We show, via a case study, that CBGPA is constraints-independent algorithm that can effectively be coupled with a WMN design model; for that, we propose a multi-objective optimization model to design WMNs topologies from scratch. The two objectives of deployment cost and average congestion of gateways are simultaneously optimized in the model. The optimization model proposed is solved using a nature inspired meta-heuristic algorithm coupled with CBGPA, which provides the network operator with a set of bounded-delay tradeoff solutions. A comparative experimental study, using large size networks (up to 169 nodes) and different key parameter settings is conducted to show the effectiveness of CBGPA and to evaluate the performance of the proposed model.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".