A Reliability-Oriented Local-Area Model for Large-Scale Wireless Sensor Networks
Bibliographic record
Abstract
Large-scale wireless sensor networks (WSNs) have demonstrated some complex features which are similar to those of other types of complex networks, such as social networks. Based on these complex features, evolution process and characteristic of WSNs, we represent a WSN topologically by building a suitable model, which is named as the reliability-oriented local-area model (ROLM) and aimed at improving the performance of WSNs. For analyzing the performance of the ROLM, we define the reliability as the probability of that the relative error between the measurement and the true value is equal to or less than ε ( ε ≥ 0 ) and proposed a parameter η to measure the reliability of the network. Based on them, we use η to analyze the influence of network structure on the reliability, and compared the reliabilities of the ROLM and the existing WSNs. Experiment results prove that the large-scale WSN follows a power-law distribution, and it has scale-free characteristic and small world characteristic. And it also shows that, comparing with existing model, ROLM not only balances energy consumption by limiting the connectivity of each node to prolong the lifetime of the network, but also improves the reliability substantially. And the ROLM can be used to express the topology of reliability-oriented WSNs and analyze the structure preferably.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".