The objective of this research is to develop new foundations of composition in heterogeneous systems, to apply these foundations in a new generation of tools for system integration, and to validate the results in experiments using automotive and avionics System-of-Systems experimental platforms. The approach exploits simplification strategies: develop theories, methods, and tools to assist in inter-layer decoupling. The research program has three focus areas: (1) theory of compositionality in heterogeneous systems, (2) tools and tool architectures for system integration, and (3) systems/experimental research. The project develops and deploys theories and methods for inter-layer decoupling that prevent or decrease the formation of intractable system-wide interdependences and maintain compositionality at each layer for carefully selected, essential system properties. Compositionality in tools is sought by exploring semantic foundations for model-based design. Systems/experimental research is conducted in collaboration with General Motors Global R&D (GM) and focuses on electric car platforms. The project is contributing to the cost effective development and deployment of many safety and security-critical cyber-physical systems, ranging from medical devices to transportation, to defense and avionics. The participating institutions seek to complement the conventional curriculum in systems science with one that admits computation as a primary concept. The curriculum changes will be aggressively promoted through a process of workshops and textbook preparation.

The objectives of this research are to design a heterogeneous network of embedded systems so that faults can be quickly detected and isolated and to develop on-line and off-line fault diagnosis and prognosis methods. Our approach is to develop functional dependency models between the failure modes and the concomitant monitoring mechanisms, which form the basis for failure modes, effects and criticality analysis, design for testability, diagnostic inference, and the remaining useful life estimation of (hardware) components. Over the last few years, the electronic explosion in automotive vehicles and other application domains has significantly increased the complexity, heterogeneity, and interconnectedness of embedded systems. To address the cross-subsystem malfunction phenomena in such networked systems, it is essential to develop a common methodology that: (i) identifies the potential failure modes associated with software, hardware, and hardware-software interfaces; (ii) generates functional dependencies between the failure modes and tests; (iii) provides an on-line/off-line diagnosis system; (iv) computes the remaining useful life estimates of components based on the diagnosis; and (iv) validates the diagnostic and prognostic inference methods via fault injection prior to deployment in the field. The development of functional dependency models and diagnostic inference from these models to aid in online and remote diagnosis and prognosis of embedded systems is a potentially novel aspect of this effort. This project seeks to improve the competitiveness of the U.S. automotive industry by enhancing vehicle reliability, performance and safety, and by improving customer satisfaction. Other representative applications include aerospace systems, electrification of transportation, medical equipment, and communication and power networks, to name a few.