Avaliação Experimental do Tempo de Formação de uma Rede Multi-salto do Padrão Wi-SUN FAN
Bibliographic record
Abstract
Resumo-Na atualidade o padrão Wi-SUN FAN (Wireless Smart Ubiquitous Network Field Area Network) está sendo de interesse para ser adotado por diferentes aplicações, por conseguir atender a demanda de larga escala de interconexão de diferentes dispositivos inteligentes.No entanto, o processo de formação da rede deste padrão é lento, o que se torna um problema para redes densas.Neste trabalho é realizada uma análise do tempo de formação da rede através de experimentos em uma rede multisalto com oito dispositivos (sete saltos), com foco nos estados de junção 1 e 3 do padrão, que utilizam os diferentes pacotes de descoberta de rede governados pelo algoritmo trickle timer.Diferentes valores de configurações adequadas para esses dois estados são verificados e discutidos, o que permite identificar as características do processo de formação da rede para este padrão.Palavras-Chave-Wi-SUN FAN, multi-salto, estados de junção, trickle timer.Abstract-Currently, the Wi-SUN FAN (Wireless Smart Ubiquitous Network Field Area Network) standard is being of interest to be adopted by different applications, to be able to meet the large-scale demand for interconnection of different smart devices.However, the network formation process of this standar is slow, which becomes a problem for dense networks.In this work, an analysis of the network formation time is performed by means of experiments on a multi-hop network with eight devices (seven hop), especially on junction states 1 and 3 of the standard which use the different network discovery frames that are governed by the trickle timer algorithm.Different values of appropriate configurations in both states are verified and discussed, which allow identifying the characteristics of the network formation process for this standard. Keywords-Wi-SUN FAN,
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".