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 objective of this research is to address fundamental challenges in the verification and analysis of reconfigurable distributed hybrid control systems. These occur frequently whenever control decisions for a continuous plant depend on the actions and state of other participants. They are not supported by verification technology today. The approach advocated here is to develop strictly compositional proof-based verification techniques to close this analytic gap in cyber-physical system design and to overcome scalability issues. This project develops techniques using symbolic invariants for differential equations to address the analytic gap between nonlinear applications and present verification techniques for linear dynamics. This project aims at transformative research changing the scope of systems that can be analyzed. The proposed research develops a compositional proof-based approach to hybrid systems verification in contrast to the dominant automata-based verification approaches. It represents a major improvement addressing the challenges of composition, reconfiguration, and nonlinearity in system models The proposed research has significant applications in the verification of safety-critical properties in next generation cyber-physical systems. This includes distributed car control, robotic swarms, and unmanned aerial vehicle cooperation schemes to full collision avoidance protocols for multiple aircraft. Analysis tools for distributed hybrid systems have a broad range of applications of varying degrees of safety-criticality, validation cost, and operative risk. Analytic techniques that find bugs or ensure correct functioning can save lives and money, and therefore are likely to have substantial economic and societal impact.

The objective of this research is to develop numerical techniques for solving partial differential equations (PDE) that govern information flow in dense wireless networks. Despite the analogy of information flow in these networks to physical phenomena such as thermodynamics and fluid mechanics, many physical and protocol imposed constraints make information flow PDEs unique and different from the observed PDEs in physical phenomena. The approach is to develop a systematic method where a unified framework is capable of optimizing a broad class of objective functions on the information flow in a network of a massive number of nodes. The objective function is defined depending on desired property of the geometric paths of information. This leads to PDEs whose form varies depending on the optimization objective. Finally, numerical techniques will be developed to solve the PDEs in a network setting and in a distributed manner. The intellectual merits of this project are: developing mathematical tools that address a broad range of design objectives in large scale wireless sensor networks under a unified framework; initiating a new field on numerical analysis of information flow in dense wireless networks; and developing design tools for networking problems such as transport capacity, routing, and load balancing. The broader impacts of this research are: helping the development of next generation wireless networks; encouraging involvement of undergraduate students and underrepresented groups, and incorporating the research results into graduate level courses. Additionally, the research is interdisciplinary, bringing together sensor networking, theoretical physics, partial differential equations, and numerical optimization.

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.

The objective of this research is to bring high levels of system reliability and integrity to application domains that cannot afford the cost, power, weight, and size associated with physical redundancy. The approach is to develop complementary monitoring algorithms and novel computing architectures that enable the detection of faults. In particular, there is a significant opportunity to reduce the reliance on physical redundancy by combining model-based and data-driven monitoring techniques. Implementing this approach to fault detection would be difficult with existing software and computing architectures. This motivates the development of a general purpose monitoring framework through monitoring-aware compilers coupled with enhancements to multi-core architectures. The intellectual merit of the project is twofold. First, it has the potential to lead to a novel fault detection approach that blends complementary monitoring algorithms. Second, advances in multi-core processors are leveraged to enable implementation of these fault detection approaches. This addresses key themes in cyber-physical systems by investigating the fundamental issue of fault detection for physical systems and by developing a generic processor architecture for monitoring. With respect to broader impact, project offers the potential for positive influences on industrial practice and education. If successful, the design ideas from this project can be incorporated into low-cost multi-core architectures suitable for embedded systems. The potentially transformative performance improvement offered by this framework could also impact current research in run-time verification and on-line monitoring. The research is to be incorporated into the course "Design, Build, Simulate, Test and Fly Small Uninhabited Aerial Vehicles" for senior undergraduate and first-year graduate students.

The objective of this research is to develop technologies to improve the efficiency and safety of the road transportation infrastructure. The approach is to develop location-based vehicular services combining on-board automotive computers, in-car devices, mobile phones, and roadside monitoring/surveillance systems. The resulting vehicular Cyber Physical Systems (CPS) can reduce travel times with smart routing, save fuel and reduce carbon emissions by determining greener routes and commute times, improve safety by detecting road hazards, change driving behavior using smart tolling, and enable measurement-based insurance plans that incentivize good driving. This research develops distributed algorithms for predictive travel delay modeling, feedback-based routing, and road hazard assessment. It develops privacy-preserving protocols for capturing and analyzing data and using it for tasks such as congestion-aware tolling. It also develops a secure macro-tasking software run-time substrate to ensure that algorithms can be programmed centrally without explicitly programming each node separately, while ensuring that it is safe to run third-party code. The research focuses on re-usable methods that can benefit multiple vehicular services, and investigates which lessons learned from this vehicular CPS effort generalize to other situations. Road transportation is a grand challenge problem for modern society, which this research can help overcome. Automobile vendors, component developers, and municipal authorities have all shown interest in deployment. The education plan includes outreach to local K-12 students and a new undergraduate course on transportation from a CPS perspective, which will involve term projects using the data collected in the project

The objective of this research is to study the formal design and verification of advanced vehicle dynamics control systems. The approach is to consider the vehicle-driver-road system as a cyber-physical system (CPS) by focusing on three critical components: (i) the tire-road interaction; (ii) the driver-vehicle interaction; and (iii) the controller design and validation. Methods for quantifying and estimating the uncertainty of the road friction coefficient by using self-powered wireless sensors embedded in the tire are developed for considering tire-road interaction. Tools for real-time identification of nominal driver behavior and uncertainty bounds by using in-vehicle cameras and body wireless sensors are developed for considering driver-vehicle interaction. A predictive hybrid supervisory control scheme will guarantee that the vehicle performs safely for all possible uncertainty levels. In particular, for controller design and validation, the CPS autonomy level is continuously adapted as a function of human and environment conditions and their uncertainty bounds quantified by considering tire-road and driver-vehicle interaction. High confidence is critical in all human operated and supervised cyber-physical systems. These include environmental monitoring, telesurgery, power networks, and any transportation CPS. When human and environment uncertainty bounds can be predicted, safety can be robustly guaranteed by a proper controller design and validation. This avoids lengthy and expensive trial and error design procedures and drastically increases their confidence level. Graduate, undergraduate and underrepresented engineering students benefit from this project through classroom instruction, involvement in the research and substantial interaction with industrial partners from the fields of tires, vehicle active safety, and wireless sensors.

The objective of this research is the development of novel control architectures and computationally efficient controller design algorithms for distributed cyber-physical systems with decentralized information infrastructures and limited communication capabilities. Active safety in Intelligent Transportation Systems will be the focus cyber-physical application. For the successful development and deployment of cooperative active safety systems, it is critical to develop theory and techniques to design algorithms with guaranteed safety properties and predictable behavior. The approach is to develop a new methodology for the design of communicating distributed hybrid controllers by integrating in a novel manner discrete-event controller design and hybrid controller design and optimization. The methodology to be developed will exploit problem decomposition and will have significant technological impact for a large class of cyber-physical systems that share features of modularity in system representation, partial information, and limited communication. The focus on distributed control strategies with limited communication among agents is addressing an important gap in existing control theories for cyber-physical systems. The approach will mitigate the computational limitations of existing approaches to control design for hybrid systems. Given the focus on cooperative active safety in Intelligent Transportation Systems, the results of this effort will have significant societal impact in terms of increased traffic safety and reduced number and severity of accidents. The broader impacts of this proposal also include involvement of high-school and undergraduate students and curriculum development by incorporating results of research into existing courses on cyber-physical systems.