A framework to extract personalized behavioural patterns of user's IoT devices data
Bibliographic record
Abstract
The growing trend of devices participation in Internet of Things (IoTs) platforms have created billions of IoT devices in both consumer and industrial environments. IoT devices form the network of devices connected to each other by communication technologies in different environments to monitor, collect, exchange, and to take actions. Due to the growth of IoT devices, it is cheaper and easily available so users started using these devices to achieve their personal goals, such as to reduce electricity cost at home. Existing research has proposed new interconnection implementation mechanisms for IoT devices to monitor environments by low cost systems. However, existing work does not investigate the historical data of IoT device usage to assist users in achieving their goals. In our research, we propose an engine that identifies the behavioural patterns of IoT device users. Our engine works in three steps: First, the engine uses a database to store the IoT devices usage data. Second, our engine prepares the data in a suitable model for data analysis. Finally, our engine analyses the represented data to extract user behavioural patterns. We perform an empirical study to evaluate our engine. Our results shows that users, on average, use less than 50% of their IoT devices at specific times and have a relatively small impact across other devices in the environment.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".