Decentralized Access Control for Smart Buildings Using Metadata and Smart Contracts
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.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".