Trust-Aware Virtual Network Embedding in Wireless Sensor Networks
Bibliographic record
Abstract
Network virtualization in wireless sensor networks (WSNs) enables the utilization of their shared sensing capabilities. Efficient assignment of WSN resources to maximize the infrastructure provider’s revenue can be achieved by virtual network embedding (VNE) while considering quality of information (QoI) (as the accuracy of sensing), quality of service (QoS) (as the reliability), and wireless interference handling constraints. Improving the acceptance ratio of VNE is essential because the more the virtual networks can be mapped onto the substrate network, the more revenue they will generate for the infrastructure provider. However, the shared and complex nature of VNE exposes the WSNs to security risks. This article develops a novel offline trust-aware virtual WSN (TA-VWSN) algorithm to maximize the virtual networks acceptance rate while minimizing the cost. This algorithm improves the QoI, QoS, and security by adding required trust level constraints to virtual nodes and links and trust level constraints to the substrate counterparts. Our algorithm embeds virtual nodes and links on substrate nodes and links with the required trust levels. The additional constraints increase the complexity of computation required to achieve an optimal solution. However, our TA-VWSN algorithm achieves a high-quality suboptimal solution in a short duration, enabling us to investigate the tradeoff between solution quality and search time. Our algorithm is also evaluated in large-scale network scenarios to verify all enforced limitations by the WSN substrate. While adding security constraints limits the acceptance ratio, the simulation results demonstrate its superiority in terms of average network throughput, measurement error efficiency, and processing time when the trust attributes are assigned, making the VNE algorithm more practical.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".