Information-Centric Networking (ICN) Based Disaster Recovery and Business Continuity (DRBC) of Bangladesh
Bibliographic record
Abstract
We are proposing an Information-Centric Networking (ICN) technology-based solution for a Disaster Recovery Management System (DRMS) for a country such as Bangladesh where disasters happen almost annually. Bangladesh has very well-developed disaster management systems, plans, and processes. Due to well-technologically based systems of disaster management in remote locations or during devastating disasters, disaster activities are delayed or failed. Geographically, Bangladesh is disaster-prone; every year, the country faces a lot of economic damage. This is important to have the DRMS ready and available to mitigate, prepare, and communicate with different groups to reduce the loss and save lives. This ICN technology-based disaster management can play an important role in Information, relief, shelters, and emergency management. Communication technology can play a vital role in technology-based disaster management. Also, during a disaster, communication systems and other related infrastructures are damaged due to power failure, and other damage facts. An ICN based Disaster Recovery and Business Continuity (DRBC) system has a higher response ratio, efficient performance, low communication overhead and path distance, and less distributed delay. Also, it has been proven that ICN is a new paradigm with mobility, security, and network traffic that can be applied to highly available secure communication. We propose an ICN based Disaster Recovery System for Bangladesh that will efficiently manage the natural and manmade disasters in the country. This proposed system can be customized and applied to other countries based on their needs.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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".