@inproceedings{7765abb6c4fd4fc79ba1096d97c3efe2,
title = "Progressive recovery from failure in multi-layered interdependent network using a new model of interdependency",
abstract = "A number of models have been proposed to analyze interdependent networks in recent years. However most of the models are unable to capture the complex interdependencies between such networks. To overcome the limitations, we have recently proposed a new model. Utilizing this model, we provide techniques for progressive recovery from failure. The goal of the progressive recovery problem is to maximize the system utility over the entire duration of the recovery process. We show that the problem can be solved in polynomial time in some special cases, whereas for some others, the problem is NP-complete. We provide two approximation algorithms with performance bounds of 2 and 4 respectively. We provide an optimal solution utilizing Integer Linear Programming and a heuristic. We evaluate the efficacy of our heuristic with both synthetic and real data collected from Phoenix metropolitan area. The experiments show that our heuristic almost always produces near optimal solution.",
keywords = "Analysis, Critical infrastructure, Inter-dependence, Modeling, Multi-layer networks, Progressive recovery",
author = "Anisha Mazumder and Chenyang Zhou and Arun Das and Arunabha Sen",
year = "2016",
doi = "10.1007/978-3-319-31664-2_38",
language = "English (US)",
isbn = "9783319316635",
volume = "8985",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "368--380",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
note = "9th International Conference on Critical Information Infrastructures Security, CRITIS 2014 ; Conference date: 13-10-2014 Through 15-10-2014",
}