Heuristic and reinforcement learning-based survivable trust-aware virtual network embedding for IoT networks
Bibliographic record
Abstract
Integrating virtual wireless sensor networks (VWSNs) with the Internet of Things (IoT) improves the quality of information (QoI) and quality of service (QoS). It manages wireless interference, critical to providing efficient and reliable services. Among the challenges in IoT-WSN virtualization, the survivable virtual network embedding (SVNE) problem stands out, as it efficiently maps a virtual network request (VNR) onto a WSN substrate while considering potential substrate failures and network security standards. This paper proposes a trust-aware fault recovery mechanism to address the security and survivability of virtualized IoT-WSN applications against physical infrastructure failures with two heuristic and intelligent approaches. Our proposed heuristic approach utilizes a node importance measurement strategy for faulty nodes based on the technique for order of preference by similarity to the ideal solution (TOPSIS) method. On the other hand, in our intelligent approach, we apply the deep Q-Learning (DQL) method to ensure end-to-end failure recovery for both nodes and links and improve physical resource utilization. To maintain cost efficiency, when a VNR experiences failure due to a fault in the physical infrastructure, its operation is restored through node/link migration without considering any backup resources. Our simulation results demonstrate that the proposed strategy effectively ensures the survivability of the VNRs, mitigates failures with our proposed failure recovery algorithms, and enhances the VNR acceptance rate.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".