A fuzzy-rule-based packet reproduction routing for sensor networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is a major challenge to transfer target sensing data efficiently to sink in Internet of things. The low-efficiency data transmission can cause low quality of service. To realize the emergent detection and periodic data gathering, the sensed data should be transferred to the sink efficiently and quickly. Recently, there are many related studies. However, there are few researches taking energy efficiency, transport delay, and network reliability into comprehensive consideration. In this article, a novel adaptive green and reliable routing scheme based on a fuzzy logic system is proposed in consideration of energy efficiency, end-to-end transport delay, and network transmission reliability. The key idea of the proposed scheme is to generate different number of renewed packet copies after certain steps according to the fuzzy inference. The fuzzy inference reflects the knowledge that the nodes in the region far to the sink and with more remaining energy initiate and transmit more packet copies concurrently by multiple routing paths to ensure the success rate of data transmission, whereas less. Thus, the high energy efficiency and low latency are obtained for data collection. Our analysis and simulation results show that adaptive green and reliable routing is more superior than the existing scheme.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it