Role of Big Data in Internet of Things Networks
Bibliographic record
Abstract
With recent advancement in cyber-physical systems and technological revolutions, internet of things is the focus of research in industry as well as in academia. IoT is not only a research and technological revolution but in fact a revolution in our daily life. It is considered a new era of smart lifestyle and has a deep impact on everyday errands. Its applications include but are not limited to smart home, smart transportation, smart health, smart security, and smart surveillance. A large number of devices connected in all these application networks generates an enormous amount of data. This leads to problems in data storage, efficient data processing, and intelligent data analytics. In this chapter, the authors discuss the role of big data and related challenges in IoT networks and various data analytics platforms, used for the IoT domain. In addition to this, they present and discuss the architectural model of big data in IoT along with various future research challenges. Afterward, they discuss smart health and smart transportation as a case study to supplement the presented architectural model.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.004 |
| 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".