Towards a Resilient Smart Home
Bibliographic record
Abstract
Today's Smart Home platforms such as Samsung SmartThings and Amazon AWS IoT are primarily cloud based: devices in the home sense the environment and send the collected data, directly or through a hub, to the cloud. Cloud runs various applications and analytics on the collected data, and generates commands according to the users' specifications that are sent to the actuators to control the environment. The role of the hub in this setup is effectively message passing between the devices and the cloud, while the required analytics, computation, and control are all performed by the cloud. We ask the following question: what if the cloud is not available? This can happen not only by accident or natural causes, but also due to targeted attacks. We discuss possible effects of such unavailability on the functionalities that are commonly available in smart homes, including security and safety related services as well as support for health and well-being of home users, and propose RES-Hub, a hub that can provide the required functionalities when the cloud is unavailable. During the normal functioning of the system, RES-Hub will receive regular status updates from cloud, and will use this information to continue to provide the user specified services when it detects the cloud is down. We describe an IoTivity-based software architecture that is used to implement RES-Hub in a flexible and expendable way and discuss our implementation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".