A User-centric Security Solution for Internet of Things and Edge Convergence
Bibliographic record
Abstract
The Internet of Things (IoT) is becoming a backbone of sensing infrastructure to several mission-critical applications such as smart health, disaster management, and smart cities. Due to resource-constrained sensing devices, IoT infrastructures use Edge datacenters (EDCs) for real-time data processing. EDCs can be either static or mobile in nature, and this article considers both of these scenarios. Generally, EDCs communicate with IoT devices in emergency scenarios to evaluate data in real-time. Protecting data communications from malicious activity becomes a key factor, as all the communication flows through insecure channels. In such infrastructures, it is a challenging task for EDCs to ensure the trustworthiness of the data for emergency evaluations. The current communication security pattern of “communication before authentication” leaves a “black hole” for intruders to become part of communication processes without authentication. To overcome this issue and to develop security infrastructures for IoT and distributed Edge datacenters, this article proposes a user-centric security solution. The proposed security solution shifts from a network-centric approach to a user-centric security approach by authenticating users and devices before communication is established. A trusted controller is initialized to authenticate and establishes the secure channel between the devices before they start communication between themselves. The centralized controller draws a perimeter for secure communications within the boundary. Theoretical analysis and experimental evaluation of the proposed security model show that it not only secures the communication infrastructure but also improves the overall network performance.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".