Distributed Smart Home Architecture for Data Handling in Smart Grid
Bibliographic record
Abstract
Smart homes form an integral part of smart grid infrastructure. Recently, large numbers of sensors have been added within smart homes to enhance home appliance automation and monitoring. This addition raises questions about where the data generated within the home should be processed. The data can be processed either by one central processor or through multiple distributed processors closer to the sensors. This paper proposes a smart home distributed architecture involving home sensors talking directly to a smart gateway installed within the home. The gateway then decides which data should be forwarded to the central processor for further analysis. A test bed is designed to highlight the advantages of this approach. An open data set is used to feed sensor data into the test setup. It is shown that the local processing of data can improve efficiency by effectively utilizing available network bandwidth. Furthermore, local processing is favorable for time-critical smart home applications, since local processing has a faster data communication round trip time as compared with that of central processing. Moreover, we argue that certain calculations, like energy usage prediction for home appliances, can effectively be done locally while the central processor can be used for coordination between different local processors.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".