Cascading failure model and resilience enhancement scheme of space information networks
Reliability Engineering & System Safety, ISSN: 0951-8320, Vol: 237, Page: 109379
2023
- 17Citations
- 8Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
Space information networks provide global users with high-speed Internet services. The failure of a small number of satellites can trigger cascading failures that can bring down the network. Therefore, enhancing the network’s resilience under cascading failures is crucial. In this paper, we establish a cascading failure model for space information networks for the first time. The gravity model-based load calculation method, entity failure mechanisms, and new survivability measures are presented. The load unbalances of satellites are the leading cause of cascading failures, and the layout of the gateways determines the final load distribution. Therefore, a load-oriented gateway layout optimization model is developed. In order to obtain better optimization performance, a gateway layout solution algorithm based on the cuckoo search is given. Exhaustive simulations are carried out in a case satellite constellation, and the results show that the proposed approach is reasonable and able to enhance the resilience of the network under cascading failures.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0951832023002934; http://dx.doi.org/10.1016/j.ress.2023.109379; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85159786173&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0951832023002934; https://dx.doi.org/10.1016/j.ress.2023.109379
Elsevier BV
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