Intelligent Trust-Based Public-Key Management for IoT by Linking Edge Devices in a Fog Architecture
Bibliographic record
Abstract
Due to memory and processing limitations, Internet-of-Things (IoT) devices require external fog servers to perform some of their tasks. However, this offloading of tasks comes at the cost of more interactions whose security cannot be guaranteed without the authentication and key management scheme. Traditional prescriptions, such as those used for securing the Web, require referring to central agents, such as certificate authorities (CA) or online certificate status protocol (OCSP) responders, that sit in the cloud. This poses many challenges, including additional communication costs and repetitive delays which work against the low latency and energy efficiency goals of edge networking. In this article, we propose a novel semidecentralized public-key management scheme for smart IoT systems in which devices intelligently decide whether to look for the keying material locally at the edge or refer to the cloud for this purpose. The result is a security architecture that links IoT devices, fog servers, and cloud, but with minimal dependency on the latter. In the proposed solution, devices work collaboratively to deliver revocation lists and digital certificates of fog servers to each other. The decision to go for edge nodes or cloud CA/OCSP responders is made intelligently by each node upon learning its neighborhood and network statistics. The core idea is based on the Web of trust, but unlike that, whenever a material is not found locally, cloud servers are queried. Experiments show that through this intelligent approach, the cost of key management operations, e.g., delay, can be reduced by up to 50%.
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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 | 0.001 |
| 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) | 0.001 | 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".