Tensor-based software-defined internet of things
Bibliographic record
Abstract
IoT exhibits characteristics of the presence of diverse physical sensing and actuating devices, complex wireless communication and networking technologies, as well as large-scale heterogeneous data generated in the physical and cyber worlds. The exponentially increasing volume of data places an unprecedent burden on the network infrastructure of IoT systems, where there are two key challenges: how to represent the heterogeneous IoT data as a concise and unified model, and extract the essential core data that are smaller for transmission but consist of the most valuable information; and how to globally and flexibly control the network devices, and dynamically reallocate the bandwidth to improve the communication link utilization ratio. To address the mentioned challenges, this article first transforms structured, semi-structured, and unstructured IoT data to a unified tensor model, and employs the HO-SVD approach for extraction of the high-quality core data. Then this article applies SDN technology to IoT for device management, and develops a transition tensor model for routing path recommendation. Finally, a smart home case study is investigated, which reveals that the proposed tensor-based software defined model is feasible and promising. It is strongly suggested that further study on combination of IoT with SDN technology and tensor algebra should be performed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".