Avaliação da propagação do sinal LoRa e desenvolvimento de um método para auxiliar o planejamento de redes IoT usando otimização do modelo de Hata
Bibliographic record
Abstract
According to the Internet of Things (IoT) paradigm, the demand for communication between devices has been growing steadily. Various communication patterns have emerged and today compete with each other for application niches. Among these standards are the wide-ranging and low-power networks (LPWANs), which have continuously aroused the interest of the information technology and telecommunications (ICT) industry. In particular, LoRa® communication technology has been gaining constant prominence, especially in applications associated with smart grids. However, the initiatives are still incipient. Considering this scenario, this work has as one of its objectives to evaluate some scenarios of propagation of the LoRa signal and based on measurements collected in the field, to use these values to feed an adapted model of propagation. This model employs the Hata technique as the basis and can be used as an information source for the planning of IoT networks in scenarios applicable to smart grids. In addition, some QoS parameters are also evaluated to characterize the physical layer behavior of the LoRa signal. Even in different scenarios, the results showed that urban cells with an approximate radius of 2 km in unlicensed band (~915 MHz) typically achieve typical packet delivery rates ranging from 75% to 100%. Based on the models produced here and in association with results found in other studies, it is estimated that LoRa technology is a strong candidate for the provision of communication infrastructure for some smart grids services and that the graphic propagation models produced here can corroborate IoT network planning activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".