Using social network analysis to understand reliability in a large network of systems

James R. Enos, Roshanak Nilchiani

    Research output: Contribution to conferencePaperpeer-review

    Abstract

    Over the past several decades, the Department of Defense (DoD) has developed an expansive network of systems that must interoperate on the battlefield to deliver value to the users. However, it is difficult to manage and understand the complex interactions between these systems and the potential impact a system failure may have on the entire network of systems. To address these problems, this paper proposes adapting social network analysis metrics, specifically the betweeness centrality, to identify key systems in the network that affect the overall interoperability of the network by serving as bridges between groups of systems. Additionally, this metric may lead affect the overall reliability of the network of DoD systems as systems with a high betweeness centrality may cause systemic failure of the network. As an example network, the paper utilized the tactical aircraft within the DoD that spans all four services and over a hundred individual systems ranging from aircraft to munitions to command and control systems. This work identified several key systems within the network that are essential to the overall operation of the network of systems. The betweeness centrality metrics calculated based on the interaction network are consistent with the qualitative assessments of the systems importance for reliability and interoperability of the overall network. This work is an initial step in broadly applying social network analysis metrics to networks of systems to provide insights into the emergent behavior of networks of systems.

    Original languageEnglish
    StatePublished - 2017
    Event2017 International Annual Conference of the American Society for Engineering Management, ASEM 2017 - Huntsville, United States
    Duration: 18 Oct 201721 Oct 2017

    Conference

    Conference2017 International Annual Conference of the American Society for Engineering Management, ASEM 2017
    Country/TerritoryUnited States
    CityHuntsville
    Period18/10/1721/10/17

    Keywords

    • Reliability
    • Social Network Analysis
    • System of Systems Engineering

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