Logo image
Back
Mapping Network States using Connectivity Queries
Conference proceeding   Open access

Mapping Network States using Connectivity Queries

Alexander Rodriguez, Bijaya Adhikari, Andres D Gonzalez, Charles Nicholson, Anil Vullikanti and B. Aditya Prakash
2020 IEEE International Conference on Big Data (Big Data), pp.778-787
12/10/2020
DOI: 10.1109/BigData50022.2020.9378355
url
https://arxiv.org/pdf/2012.03413View
Open Access

Abstract

Can we infer all the failed components of an infrastructure network, given a sample of reachable nodes from supply nodes? One of the most critical post-disruption processes after a natural disaster is to quickly determine the damage or failure states of critical infrastructure components. However, this is nontrivial, considering that often only a fraction of components may be accessible or observable after a disruptive event. Past work has looked into inferring failed components given point probes, i.e. with a direct sample of failed components. In contrast, we study the harder problem of inferring failed components given partial information of some 'serviceable' reachable nodes and a small sample of point probes, being the first often more practical to obtain. We formulate this novel problem using the Minimum Description Length (MDL) principle, and then present a greedy algorithm that minimizes MDL cost effectively. We evaluate our algorithm on domain-expert simulations of real networks in the aftermath of an earthquake. Our algorithm successfully identifies failed components, especially the critical ones affecting the overall system performance.
 Big Data critical infrastructure networks data mining Greedy algorithms Local area networks Network inference Noise measurement Probes Redundancy System performance

Details

Metrics

23 Record Views
Logo image