The significant impact of a set of topologies on wireless sensor networks
Bibliographic record
Abstract
Routing and topology control for Wireless Sensor Networks (WSNs) is significantly important to achieve energy efficiency in resource-constrained WSNs, and high-speed packet delivery. In this article, we introduce a framework for WSN that combines three design approaches: (1) clustering, (2) routing, and (3) topology control. In this framework, we implement an energy-efficient zone-based topology and routing protocol. The framework features a new set of graphs referred to as the Mini Gabriel (MG) graphs. The simulation results demonstrate that the framework with the MG graphs and without these graphs are generally 28% better than the framework with an existing geometric graph. This is in terms of the total network energy consumptions. In addition, the proposed framework is 10, 25, 26, and 46% better than the proposed work with an existing geometric graph in terms of the end-to-end data transmission delay, the transmission energy consumptions, the number of hops in established paths and the routing delay, respectively. Moreover, the MG demonstrates that it achieves the connectivity property, which is critical for WSNs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".