No abstract

No abstract

Increasingly the systems engineering community recognizes that success of many of today's enterprises depends on our ability to effectively reason about and harness Systems of Systems (SoS) capabilities. This paper specifically addresses the challenges in conducting trade‐off analysis for SoS, approaches that help to alleviate these challenges, and identifies opportunities for further development. For large SoS the use of lightweight analysis approaches provides a way to manage the complexity and identify promising options for more detail trade‐off analysis approaches.

The complexity of modern systems of systems (SoS) requires the ability to quickly and effectively evaluate robustness in architecture alternatives. This article presents a framework for rapid, quantitative comparisons of robustness in SoS architectures that leverages complex network methods for assessing robustness and design of experiments (DoE) techniques for validating their use in the scenario of interest. We consider both single-layer and multilayer network representations of SoS and focus on algebraic connectivity, inverse average path length, and largest connected component size as measures of robustness. Two case studies are used to illustrate our framework and assess its utility: a command, control, communications, computer, intelligence, surveillance, and reconnaissance (C4ISR) simulation and a multilayer message-passing network simulation. We find that most of the considered network metrics capture expected robustness trends, though their ability to capture these trends is often affected by the scenario of interest. These results demonstrate the potential value of complex network methods for lightweight analysis of robustness in SoS architecture alternatives, when appropriately supported by DoE methods for understanding their limitations. Index Terms-Complex networks, design of experiments, robustness, systems of systems, systems of systems architecture. I. INTRODUCTIONM ODERN systems are becoming increasingly networked to form systems of systems (SoS), providing novel capabilities across many domains. More specifically, an SoS is "a system-of-interest whose system elements are themselves systems; typically these entail large scale inter-disciplinary problems with multiple, heterogeneous, distributed systems" [1]. For example, consider a team of low-cost, heterogeneous autonomous systems with various sensors and data links working together to perform a surveillance mission. Other examples include intelligent transportation systems and smart cities utilizing data from a network of distributed sensors to improve operations. These networked systems have the potential to improve performance and efficiency relative to complex, monolithic systems of Manuscript

No abstract