Service offloading oriented edge server placement in smart farming
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
Summary Currently, smart farming has been established to realize agriculture automation by leveraging sensors to gather the growth and environmental data for crops, and realizing multiple intelligent controls, such as irrigation, fertilization, and so on, to increase the crop yields. To support real‐time intelligent controls, edge computing is introduced to smart farming by endowing computing and storage capacities to edge devices nearby the geographically distributed sensors. However, the farmers are relatively willing to purchase and deploy a small quantity of edge servers (ESs) in the farm from the perspective of expenditure saving, thereby leading to a key challenge to guarantee the performance of the real‐time controls and the overall edge services. In view of this challenge, a service offloading‐oriented ES placement method for supporting smart farming, called SOP, is proposed to optimize the data transmission delay from sensors to ESs and the load balance among ESs. More precisely, the corresponding service range of a certain ES is ascertained according to the specific analysis of the farming service requirements. Subsequently, the layout policies for the trade‐offs of the ES performance and service efficiency are acquired. Then the most balanced policy is determined as the final ES placement strategy. Eventually, we evaluate the performance of the whole ES system and the service execution efficiency with SOP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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