Lightweight Scheme for Smart Home Environments using Offloading Technique
Bibliographic record
Abstract
Internet of Things (IoT) as an emerging technology has been transforming the different aspects of our world from simple preprogrammed coffee machine to smart farming. Due to the human nature to simplify and ease of living, human are becoming dependent on these automated IoT devices and smart environments like smart phones, wearable devices, smart home and etc. In order to provide better QoS, these devices needs to work together and share data among them, also to the service providers and the cloud. Since these devices are resource constrained, IoT technology heavily depends on the cloud for processing, analytics and storage. But these data coming from the devices contains lot of personal identity information (PII). Almost all the time, the users of these devices are unaware of these information that is being transmitted or they do not possess the control over the data that they are being sent to the service provider, as well as to the cloud. Even the cloud services and service providers are secured but they are always curious. There are lot of security measures implemented for end to end communication but IoT lacks the mechanism for securing the data that the devices are generating along with access control. In this article we are proposing an approach for the security, privacy and access control of user data using Attribute Based Encryption (ABE) in smart home as the case study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".