Context-Aware Adaptive Remote Access for IoT Applications
Bibliographic record
Abstract
The rapid growth of communication networking, ubiquitous sensing, and signal processing has spurred the emergence of the Internet of Things (IoT) era. As a novel cutting-edge technology, the IoT enables a plethora of smart-devices equipped with diverse computing, sensing, and actuation capabilities to be connected to the Internet. Thus, it promises to provide a revolutionary and fully connected “smart” world while greatly developing economies and enhancing the quality of life. IoT is indeed an emergent global phenomenon, where real-time remote access to data and applications opens new unprecedented opportunities for ubiquitous monitoring and managing. In such dynamic, interconnected, and heterogeneous environment where the context conditions (location, time, situation sensitivity, etc.) are continuously and frequently changing, context-aware and adaptive solutions for data access are required to respond to the applications' needs. Nevertheless, until now, no schemes provide concrete context-aware access control mechanisms in IoT. In this article, we design a novel context-aware attribute-based access control (CAABAC) that considers the dynamic context changes. The proposed approach incorporates the contextual information with the ciphertext-policy attribute-based encryption (CP-ABE) to guarantee adaptive contextual access to data. The extensive analysis and simulations prove both the effectiveness and efficiency of the proposed scheme. Specifically, context-aware and adaptive remote access is enabled while outperforming other benchmarked schemes in terms of storage, communication, and computational cost.
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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 | 0.002 |
| 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".