Spatiotemporal Risk-Averse Routing

Farabi Muhammad Iqbal, Fernando Kuipers

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

12 Citations (Scopus)
162 Downloads (Pure)

Abstract

A cyber-physical system is often designed as a network in which critical information is transmitted. However, network links may fail, possibly as the result of a disaster. Disasters tend to display spatiotemporal characteristics, and consequently link availabilities may vary in time. Yet, the requested connection availability of traffic must be satisfied at all times, even under disasters. In this paper, we argue that often the spatiotemporal impact of disasters can be predicted, such that suitable actions can be taken, before the disaster manifests, to ensure the availability of connections. Our main contributions are three-fold: (1) we propose a generic grid-based model to represent the risk profile of a network area and relate the risk profile to the availability of links and connections, (2) we propose a polynomial-time algorithm to identify connections that are vulnerable to an emerging disaster risk, and (3) we consider the predicted spatiotemporal disaster impact, and propose a polynomial-time algorithm based on an auxiliary graph to find the most risk-averse path under a time constraint.
Original languageEnglish
Title of host publication2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSH)
Subtitle of host publication2016 IEEE Infocom CPSS Workshop
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)978-1-4673-9955-5
DOIs
Publication statusPublished - Apr 2016
Event IEEE INFOCOM Workshop on Cross-Layer Cyber Physical Systems Security (CPSS) -
Duration: 11 Apr 2016 → …

Workshop

Workshop IEEE INFOCOM Workshop on Cross-Layer Cyber Physical Systems Security (CPSS)
Period11/04/16 → …

Keywords

  • Spatiotemporal phenomena
  • Earthquakes
  • Conferences
  • Time factors
  • Cyber-physical systems
  • Prediction algorithms
  • Hurricanes

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