Disaster resilience of optical networks: State of the art, challenges, and opportunities
Bibliographic record
Abstract
For several decades, optical networks, due to their high capacity and long-distance transmission range, have been used as the major communication technology to serve network traffic, especially in the core and metro segments of communication networks. Unfortunately, our society has often experienced how the correct functioning of these critical infrastructures can be substantially hindered by massive failures triggered by natural disasters, weather-related disruptions and malicious human activities. In this position paper, we discuss the impact on optical networks of all major classes of disaster events mentioned above, and we overview recent relevant techniques that have been proposed to increase the disaster resilience of optical networks against the various classes of disaster events. We start by presenting some proactive methods to be applied before the occurrence of a disaster. Then we move our focus also on other preparedness methods that can be executed in the (typically short) time frame between the occurrence of an early alert of an incoming disaster and the time a disaster actually hits the network. Finally, we discuss reactive procedures that allow performing post-disaster recovery operations effectively. The analysis of disaster resilience mechanisms provided in this paper covers both wired and optical wireless communication infrastructures and also contains explicit remarks covering the role of emerging technologies (e.g., fixed-mobile convergence in the 5G era and beyond) in disaster resilience.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 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".