A User-Centered Security Approach to Create an IoT based Multi-Layered Fog-Cloud Architecture for Data Optimization in Raised Bed Farming
Bibliographic record
Abstract
Cloud Computing and Internet of Things (IoT) are popularly intertwined to create a network of smart systems, especially in the field of Agriculture. As agricultural industries rise, augmented use of heterogeneous communication and sensing on the field would be needed. For all this data to be transferred and processed on the cloud efficiently, low latency rates, consistently high bandwidths, minimal congestion, etc. would be needed, which, is still a complication with the current cloud computing models. This paper proposes a multi-layered fog architecture that detects outliers in the data received from the sensing environment based on three categories: Classification, Isolation, and Clustering and then aggregates it before sending it to the cloud. The architecture works closely in a user-centered design approach that connects the farmers and analysts to the fog allowing them to create an automated agricultural system. With the help of Fog, processing abilities are brought closer to the data source which reduces the load on cloud resources, thereby making the overall system a lot more efficient and secured. This paper also presents a prototype of the interface that can be used to monitor and control IoT devices on the field as well as define fuzzy rules for the agricultural system.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".