Towards a System of Systems Engineering Architecture Framework

Model-based systems engineering (MBSE) is a discipline of systems engineering in which the model forms the heart of all the systems engineering activities and is the basis of many of the project artefacts. Systems modeling is no longer viewed as simply a “good idea” but is becoming an increasingly important part of any systems engineering project. The application of MBSE is becoming well understood at the systems level; however, there is a lack of research and subsequent industrial application at the system of systems (SoS) level. This paper presents a model-based approach to requirements engineering for SoSs. The approach is derived from an MBSE approach to requirements engineering and therefore represents current best practice in SoSs in terms of established standards and research. This paper builds upon and is an evolution of the initial foundations for model-based requirements engineering for systems of systems that were published in an earlier paper.

Behavior models are key models in SoS (System of Systems) modeling and simulation in aircraft top-level demonstration. After architecture modeling to comprehensively describe the SoS, it is essential to transform the models for SoS mission simulation to achieve the virtual verification and validation in MBSE (Model-Based System Engineering). With the analysis of behavior modeling, two methods are introduced. Driving the entities in mission simulation system directly by the state machine models in SoS architecture after the interface definition is a proven method. Through the format conversion of the SoS architecture outputs, the operational/system state diagrams can be transformed to the corresponding organizations/systems behavior models in the mission simulation system. A sample case is provided to illustrate the continuous behavior modeling by format conversion in an air-to-surface attack mission. Compared to the traditional manual behavior modeling, the format conversion method leaves out the repeated behavior modeling and saves the corresponding working load.KeywordsBehavior modelingSystem of systemsModel transformation

This paper is concerned with the engineering of systems that are themselves comprised of other component systems, and where each of the component systems serves organizational and human purposes.i¾ i¾ These component purposes may be locally managed and optimized independently, or nearly so, of the objectives to be met by the composite system.i¾ i¾ There are a number of inherent characteristics of these systems, and such related terms as systems of systems (SOS) or federations of systems (FOS) or federated systems of systems (F-SOS) are often used to characterize them.i¾ i¾ It is asserted that the resultant systems generally possess the characteristics of complex adaptive systems.i¾ i¾ We provide an overview of the literature describing these engineering efforts and provide plausible strategies for systems engineering and management of SOS and FOS that are based on the principles of a 'new federalism'.i¾ i¾ Finally, the implications of these plausible SOS and FOS systems engineering and management concepts are discussed with emphasis on evolutionary acquisition in the style of DoD and Intelligence Community related programs.

This article describes the emerging roles of the systems engineering (SE) function in supporting enterprise management in information‐intensive organizations. “Enterprise Systems Engineering” (ESE) comprises three major roles or “levels” of systems engineering for successful and efficient development or procurement of large complex systems of systems (SoS). While the authors' experience focuses predominantly on government organizations acting as their own SoS integrators, the SoS ESE concept has generic applicability for any organization, public or private, seeking to attain competitive advantage through leveraging of information technology resources and systems. The processes and tools described here have been developed and successfully employed to facilitate government project management and investment decisions and control. [Carlock and Decker, 1998] This paper describes a formal three‐level SoS ESE process that, at the top level, organizes and maintains all of the details of the enterprise‐wide SoS architecture and strategic development plan in a flexible framework that accommodates the changes expected over a long SoS evolution. The information maintained in this framework allows the organization to know where it is going, how and when it is going to get there, the required capabilities and interfaces of each SoS component, and the impact of changes to system requirements, budgets, schedules, etc., on the overall SoS. The middle level processes allow the organization to perform trade studies among alternative solutions to implement required capabilities based on what is best for the enterprise‐wide SoS rather than just local considerations. The end result of the middle level processes is a selected and approved solution and its associated cost, schedule, benefits, and technical baselines. The third level processes implement the approved solutions in accordance with the approved baselines. © 2001 John Wiley & Sons, Inc. Syst Eng 4: 242–261, 2001

To keep pace with growing system complexity, Digital Engineering (DE) Model Based Systems Engineering (MBSE) capabilities must be expanded. A digital thread based approach is required to holistically evaluate measures of effectiveness/performance for missions. This paper describes the approach taken to connect requirements, SysML‐based architecture, and analytical Modeling and Simulation (M&S) yielding an interdisciplinary digital thread supporting mission analysis. The analysis was conducted in support of a multi‐domain (land, air, sea, and space) multi‐platform mission to detect, track, and engage the enemy. The approach used commercially available tools, strategically connected to establish an Authoritative Source of Truth (ASOT) for each piece of data, and gain insights about the System of Systems (SoS) beyond that which the component models could provide on their own. The results of these efforts produced a multipurpose SoS MBSE toolset enabling semi‐automated trade study analysis, while more importantly serving as a basis from which to expand systems engineering activities during subsequent phases of the systems' lifecycle. This paper concludes by identifying further opportunities for MBSE research and development to expand the developed capability.

In traditional Systems Engineering, the Systems Engineer acts as the overall system creator. Primarily through stakeholder interaction, requirements analysis, and architectural design, the Systems Engineering shapes and influences the system solution through specifications, top-down design, and bottoms-up integration and test. With the large-scale introduction of commercial-off-the-shelf (COTS) components, the Systems Engineer acts more like a composer. The top-down requirements and design activities need to be adjusted to account for the bottoms-up constraints imposed by the COTS technology. The System Engineer shapes and influences COTS-based developments through requirements modifications and negotiations, along with simultaneous system design, integration, and test. This paper explores the changing role of the Systems Engineer in today's complex system of systems (SoS) environment. We will use the analogies of conductor and choreographer to describe the System Engineering leadership challenges that exist in a SoS environment. Similar to a conductor, we will look at the set of activities that ensure the elements in the SoS work together in harmony. Similar to a choreographer, we will look at the set of activities that ensure the expressive nature of each of the SoS elements is fully realized, while still conforming to its overall objectives. Using these analogies, we will draw some conclusions for the evolving Systems Engineering role in a SoS environment. We will also explore which of these activities emerge as dominant in situations both with and without a lead SoS integrator.

Top-down decomposition of a complex system of systems (SoS) requires representation of the behavior of multiple individuals and cyber-physical systems performing a wide variety of tasks, usually at different times and with different goals. In the future, as humans and machines learn and co-adapt in their mission context, systems engineering must develop approaches that support dynamic analysis and synthesis in both the design and operation of systems. The concept of a “digital (mission) twin” provides an integration framework for design and control of future complex cyber-physical-human SoS. The framework builds on mission function task (MFT) analysis, providing a basis for model-based systems engineering (MBSE) activities to identify and address needs for automation or other forms of advanced technology that improve overall system performance. However, human-driven adaptation and eventually machine adaptation of complex SoS poses organizational and methodological challenges. We discuss the need for and opportunities for convergence of three often segregated SE methodologies: product engineering, human systems integration, and mission/operations analysis by expanding the product or process engineering view of a digital twin to the mission level. In the present this gives us a framework to analyze and specify automation and adaptation opportunities at the mission level. In the future we envision this twin continuously operates with the real systems to manage both emergent mission conditions and lifecycle adaptation. We applied this framework to a complex military mission using a case where artificial intelligence can be incorporated widely across a set of mission tasks to significantly improve performance.

The exploration of Systems of Systems (SoS) modeling using Model‐Based Systems Engineering (MBSE) is an ongoing area of research, wherein researchers are actively investigating various frameworks and languages suitable for initiating SoS modeling. Urban Air Mobility (UAM) represents a novel approach to urban transportation, integrating multiple systems and stakeholders to enable on‐demand, environment friendly, efficient, and sustainable air travel within cities. International organizations have dedicated significant efforts to develop a comprehensive Concept of Operation (ConOps) for UAM, aiming to define the UAM ecosystem and identify the supporting systems. While previous research was focused on defining the Urban Air System (UAS) using Systems Modeling Language (SysML), there remains a gap in capturing the ConOps for UAM as a System of Systems (SoS) using SysML. This study seeks to address this gap by utilizing the MagicGrid Framework for SoS modeling.

Systems of Systems (SoS) modeling is becoming increasingly important in both civilian and military systems. The Department of Defense (DoD) Defense Acquisition Guidebook, [1] defines a SoS as a “set or arrangement of systems that results when independent and useful systems are integrated into a larger system that delivers unique capabilities.” Organizations are changing their emphasis from “We need a new system” to “We need to achieve a specific outcome.” As these outcomes become more complex and the associated systems more complex, the management, modeling and simulation of these SoS becomes equally challenging. Often, the SoS is modeled in all its complexity, often at a single level of abstraction or level of detail. Instead of a “megamodel” approach, a standards-based “model of models” approach is what is necessary. This approach will use the Object Management Group (OMG) Unified Profile for DoDAF and MODAF (UPDM) for modeling enterprise architectures from capabilities to detailed components, and the Reusable Asset Specification (RAS) for defining reusable assets. Combining UPDM and RAS provides a Model of Models approach with the main model specifying assets in various levels of details. The models specified by these assets can be referenced when detailed analysis is required, or hidden when a SoS viewpoint is required, allowing the analyst to see the forest through the trees. The paper will also include an assessment of the applicability and effectiveness of this approach. The International Conference on Systems Engineering (INCOSE) System of Systems Working Group (SoSWG) has collected a set of “Pain Points on SoS” from a variety of international sources. This paper will review these pain points and discuss how the application of a Model of Model approach, combined with standards-based modeling tools and a reusable assets approach can help to alleviate some of the pain currently being felt by SoS architects and managers. Hopefully, this response to their SOS will deliver some much-needed assistance.

Systems engineering is a transdisciplinary scientific and applied direction that combines the principles of a systems approach, methods, tools and standards that provide an integrated methodology and relevant technologies for the design and development of complex systems, their comprehensive analysis and verification, efficient and safe use. The modern approach to systems engineering is characterized by an accentuated modeling of systems throughout their life cycle with the ultimate goal of creating their digital twins. It is called model-based systems engineering (MBSE). Consistent integration of MBSE with software engineering leads to the formation of the MBSSE (Model-Based Systems and Software Engineering) direction. This textbook is devoted to this area, the main objective of which is to study the conceptual and scientific-methodological foundations of MBSE and MBSSE, models and methods of life cycle management of systems and software, basic standards of systems engineering, study and mastery of the UML and SysML systems modeling languages and the corresponding modeling tools, study of requirements engineering methods and development of a description of the system architecture, familiarization with the concept of digital twins and their role in MBSE, study of the MBSSE reference model and its relationship with the process standards of systems engineering, study of the methodological aspects of the system and software integration process. The final chapter of the textbook is devoted to the study of the mathematical foundations of systems engineering. The main aspects of the mathematical theory of systems by A. Wayne Wymore, which became an important stimulus for the development of the model-oriented approach in systems engineering, and modern research, including the apparatus of finite automata by David Harel, the formalism of modeling of discrete systems DEVS, research into category theory as a formal mathematical basis for model-based system design are considered. The authors recommend this course as a basic course for training IT professionals.

This describes a system of systems (SoS) systems engineering (SE) artifacts, compares and contrasts them with similar ones developed and used for individual systems, and explains how they are used to guide SoS engineering processes. This concludes with next steps for using SoS artifacts to continue maturing the understanding of SoS SE in an international cooperative effort with the United Kingdom, Australia, and Canada.

Conventional disaster response paradigms are fundamentally constrained by reliance on human-centric intelligence, which introduces significant cognitive and heuristic biases, resulting in sub-optimal decision-making and inefficient resource allocation under high-stress, dynamically evolving scenarios. This research posits a novel framework that transcends these limitations by operationalizing a cyber-physical System of Systems (SoS) architecture where a heterogeneous fleet of Uncrewed Systems (UxS) functions as a high-fidelity Digital Twin (DT). The cognitive core of this framework is a Model-Based Artificial Intelligence (MBAI) engine, a synergistic integration of Model-Based Systems Engineering (MBSE) methodologies with advanced AI. This MBAI leverages pre-compiled Pattern Libraries (PL), constraint-based mathematical models, and predictive physics-based and stochastic simulations (ModSim) to explore potential state-space evolutions and derive optimal control policies. The DT provides a real-time, synchronous emulation of the physical environment by assimilating multi-modal data streams from UxS sensor suites and disparate inter-agency systems. The system provides the functional mechanisms to enforce data consistency across a federated architecture, thereby adhering to the single source of truth principle, and meticulously documents data lineage to maintain a forensically sound chain of custody for all collected data objects. Architecturally, the complex UxS fabric is conceptualized using an SoS methodology and decomposed via the rigorous Systems Engineering (SE) Vee model to ensure robust integration, verification, and validation of all subsystems. Operationally, the MBAI-driven DT autonomously establishes comprehensive situational awareness, performs multi-objective optimization to orchestrate a minimum viable plan, and dynamically allocates assets to mitigate impacts in high-vulnerability sectors. The system is designed to interface directly with critical infrastructure nodes such as power, communication, and transportation to model interdependencies, predict and preempt cascading failures, and reinforce Emergency Support Functions (ESF). Initial field deployment of physical UxS assets focuses on high-resolution geospatial data acquisition to calibrate and validate the DT's underlying models. The longitudinal, high-fidelity data generated throughout the response lifecycle (from heroic to disillusionment phases) is invaluable for post-hoc forensic analysis, model refinement, and provides a quantitative, auditable basis for Federal Emergency Management Agency (FEMA) assessments and insurance claims adjudication, ensuring verifiable data provenance and maintaining a strict chain of custody for evidentiary purposes. This extensible framework is agnostic to disaster typology and is engineered to enhance operational resiliency across a spectrum of catastrophic events, from hydrometeorological and geophysical events to technological and anthropogenic crises.

A System of Systems (SoS) is a term used to describe independent systems converging for a purpose that could only be carried out through this interdependent collaboration. Many examples of SoSs exist, but the term has become a source of confusion across domains. Moreover, there are few illustrative SoS examples demonstrating their initial classification and structure. While there are many approaches for engineering of systems, less exist for SoS engineering. More specifically, there is a research gap towards approaches addressing SoS security risk assessment for engineering and operational needs, with a need for tool-support to assist modelling and visualising security risk and requirements in an interconnected SoS. From this, security requirements can provide a systematic means to identify constraints and related risks of the SoS, mitigated by human-user and system requirements. This work investigates specific challenges and current approaches for SoS security and risk, and aims to identify the alignment of SoS factors and concepts suitable for eliciting, analysing, validating risks with use of a tool-support for assessing security risk in the SoS context.

As our knowledge of system of systems (SoS) has grown and evolved, so has our understanding of how to engineer and manage them. In systems engineering, we develop architectures and frameworks to bring meaning to this kind of uncertainty, but for SoS engineering (SoSE) we are still in search of how we can structure this understanding. In this paper, we review the SoS literature to illustrate the need to create an SoSE management framework based on the demands of constant technological progress in a complex dynamic environment. We conclude from this review that the history and evolution of defining SoS has shown that: (1) SoS can be defined by distinguishing characteristics and (2) SoS can be viewed as a network where the ldquobest practicesrdquo of network management can be applied to SoSE. We use these two theories as a foundation for our objective to create an effective SoSE management framework. To accomplish this, we utilize modified fault, configuration, accounting, performance, and security (FCAPS) network principles (SoSE management conceptual areas). Furthermore, cited distinguishing characteristics of SoS are also used to present a SoSE management framework. We conclude with a case analysis of this framework using a known and well-documented SoS (i.e., Integrated Deepwater System) to illustrate how to better understand, engineer, and manage within the domain of SoSE.

<title>ABSTRACT</title> <p>The International Council on Systems Engineering <ext-link ext-link-type="uri" xlink:href="https://www.incose.org/">https://www.incose.org/</ext-link> is a recognized standards body defining a system engineering knowledge-base, but this knowledge falls short of fully recognizing manufacturing in the Systems Engineering (SE) framework. To be inclusive, Manufacturing needs to join in the initiative of Model Based Systems Engineering to be relevant and succeed in the digital transformation in the field of systems engineering. This paper addresses this need in manufacturing by applying Model Based System Engineering (MBSE) to the identification and management of key characteristics so that a more relevant set of Manufacturing requirements can be introduced into the MBSE construct and help realize manufacturing resilience and become a full SE partner.</p> <p><bold>Citation:</bold> “Identification and Management of Key Characteristics in Product Development Using Model Based Systems Engineering,” In <italic>Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium</italic> (GVSETS), NDIA, Novi, MI, Aug. 15-17, 2023.</p>