Decentralized Access Control for Smart Buildings Using Metadata and Smart Contracts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Managing the privileges of occupants and visitors of large commercial buildings to access different building areas, control systems and equipment therein is a challenging task. The best practice today involves giving long-term building occupants, for example employees working in the building, access privileges to their organization areas and requiring visitors to be escorted by them. This approach is conservative and inflexible. Ideally, an automated solution is needed to manage access delegations; however, traditional role-based access control models are unwieldy in that they require the specification of all roles and their relative authority, which is a challenge in large buildings home of multiple organizations and numerous visitors. In this paper, we present a methodology based on blockchain smart contracts to describe, grant, and revoke fine-grained permissions for building users in a decentralized fashion. This method supports access control using resource description framework (RDF) graphs and implements two APIs for client applications. Leveraging the metadata of a real building, we have applied the proposed method to manage privileges in some realistic use-cases and shown that it can greatly reduce the administration overhead while providing fine-grained access control.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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 it