Distributed Trust-Aware Virtual Network Embedding for Industrial IoT Systems
Bibliographic record
Abstract
Network virtualization in wireless sensor networks (WSNs) enables the utilization of shared sensing capabilities in many industrial internet of things (IIoT) applications. Efficient assignment of WSN resources can be achieved through virtual network embedding (VNE) while considering the Quality of Information (QoI) (as the accuracy of sensing), the Quality of Service (QoS) (as the reliability), and wireless interference handling constraints. The shared and complex nature of VNE exposes WSNs to security risks. In this paper, we develop a novel offline distributed trust-aware virtual wireless sensor networks (DTA-VWSN) algorithm that considers the QoI, QoS, and security, by adding required trust level constraints to virtual nodes and links and trust level constraints to the substrate counterparts. Since centralized algorithms suffer from scalability issues, this paper presents our new approach to the virtual network embedding problem in a distributed manner. We use the techniques of multi-agent systems as a well-known approach for distributed systems to scale these algorithms to network size. Our simulation results show that DTA-VWSN improves the execution time of embedding algorithms, acceptance ratio, and cost in substrate networks.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".