Title: CPS: Breakthrough: Compositional Modeling of Cyberphysical Systems This project is devoted to the discovery of new mathematical modeling techniques for Cyber-Physical Systems. In particular, the research involves devising novel conceptual methods for assembling systems from subsystems, and for reasoning about the behavior of systems in terms of the behavior of their subsystems, which may be computational or physical. The results enable scientists and engineers to develop more realistic models of the systems they are designing, and to obtain greater insights into their eventual behavior, without having to build costly prototypes. The intellectual merits are the new notions of system behavior being developed that unify the computational and the physical, and the mathematical operators and laws governing the relationships between systems and subsystems. The project's broader significance and importance lie in the increased pace of innovation within Cyber-Physical System design that the new modeling techniques make possible, and the curricular enhancements that the novel conceptual frameworks under development support. The specific research program of this project involves the development of a novel modeling paradigm, Generalized Synchronization Trees (GSTs), into a rich framework for both describing Cyber-Physical Systems (CPSs) and studying their behavior under interconnection. GSTs are inspired by Milner's use of Synchronization Trees (STs) to model interconnected computing processes, but GSTs generalize the mathematical structure of their forebears in such a way as to encompass systems with discrete ("Cyber") as well as continuous ("Physical") dynamics. As Milner did with STs, the PIs are developing an algebraic theory of composition for GSTs. Such theories have a particular advantage over non-algebraic ones: because the composition of two (or more) objects results in an object of the same type, composition operators can be nested to build large structures out of smaller ones. Thus, the theory of GSTs is inherently compositional. The development of the theory involves five distinct but complementary endeavors. Standard models for cyber-physical systems are being encoded as GSTs in a semantically robust way; meaningful notions of composition and congruence for CPSs are being described and studied algebraically; the interplay between behavioral equivalence and the preservation of system properties is being investigated; a notion of real-time (or clock time) is under consideration for GSTs; and GSTs are being assessed as modeling tools for practical design scenarios.

This project represents a cross-disciplinary collaborative research effort on developing rigorous, closed-loop approaches for designing, simulating, and verifying medical devices. The work will open fundamental new approaches for radically accelerating the pace of medical device innovation, especially in the sphere of cardiac-device design. Specific attention will be devoted to developing advanced formal methods-based approaches for analyzing controller designs for safety and effectiveness; and devising methods for expediting regulatory and other third-party reviews of device designs. The project team includes members with research backgrounds in computer science, electrical engineering, biophysics, and cardiology; the PIs will use a coordinated approach that balances theoretical, experimental and practical concerns to yield results that are intended to transform the practice of device design while also facilitating the translation of new cardiac therapies into practice. The proposed effort will lead to significant advances in the state of the art for system verification and cardiac therapies based on the use of formal methods and closed-loop control and verification. The animating vision for the work is to enable the development of a true in silico design methodology for medical devices that can be used to speed the development of new devices and to provide greater assurance that their behaviors match designers' intentions, and to pass regulatory muster more quickly so that they can be used on patients needing their care. The scientific work being proposed will serve this vision by providing mathematically robust techniques for analyzing and verifying the behavior of medical devices, for modeling and simulating heart dynamics, and for conducting closed-loop verification of proposed therapeutic approaches. The acceleration in medical device innovation achievable as a result of the proposed research will also have long-term and sustained societal benefits, as better diagnostic and therapeutic technologies enter into the practice of medicine more quickly. It will also yield a collection of tools and techniques that will be applicable in the design of other types of devices. Finally, it will contribute to the development of human resources and the further inclusion of under-represented groups via its extensive education and outreach programs, including intensive workshop experiences for undergraduates.

This project represents a cross-disciplinary collaborative research effort on developing rigorous, closed-loop approaches for designing, simulating, and verifying medical devices. The work will open fundamental new approaches for radically accelerating the pace of medical device innovation, especially in the sphere of cardiac-device design. Specific attention will be devoted to developing advanced formal methods-based approaches for analyzing controller designs for safety and effectiveness; and devising methods for expediting regulatory and other third-party reviews of device designs. The project team includes members with research backgrounds in computer science, electrical engineering, biophysics, and cardiology; the PIs will use a coordinated approach that balances theoretical, experimental and practical concerns to yield results that are intended to transform the practice of device design while also facilitating the translation of new cardiac therapies into practice. The proposed effort will lead to significant advances in the state of the art for system verification and cardiac therapies based on the use of formal methods and closed-loop control and verification. The animating vision for the work is to enable the development of a true in silico design methodology for medical devices that can be used to speed the development of new devices and to provide greater assurance that their behaviors match designers' intentions, and to pass regulatory muster more quickly so that they can be used on patients needing their care. The scientific work being proposed will serve this vision by providing mathematically robust techniques for analyzing and verifying the behavior of medical devices, for modeling and simulating heart dynamics, and for conducting closed-loop verification of proposed therapeutic approaches. The acceleration in medical device innovation achievable as a result of the proposed research will also have long-term and sustained societal benefits, as better diagnostic and therapeutic technologies enter into the practice of medicine more quickly. It will also yield a collection of tools and techniques that will be applicable in the design of other types of devices. Finally, it will contribute to the development of human resources and the further inclusion of under-represented groups via its extensive education and outreach programs, including intensive workshop experiences for undergraduates.

Traditionally, the design of urban transit services has been based on limited sampling data collected through surveys and censuses, which are often dated and incomplete. Lacking massive online feeds from multiple transit modes makes it hard to achieve real-time equilibrium in demand and supply relationship through cyber-control, which eventually manifests into multiple urban transportation issues: (i) lengthy last-mile transit due to non-supply, (ii) prolonged waiting due to undersupply, and (iii) excessive idle mileage due to oversupply. This project addresses these issues by focusing on two types of transportation systems in metropolitan areas: (i) public bike rental sharing systems and (ii) fleet-oriented ride sharing systems. The public bike rental sharing systems are used to allow commuters to rent bikes near public transit stations for the last mile of their trips. The fleet-oriented ride sharing systems schedule a fleet of participating vehicles for ride sharing among passengers in which shared ridership reduces individual fare paid by passengers, increases the profit of taxi drivers, and can improve the availability of service. The theory and practice of transportation sharing systems have typically focused on isolated individual transportation modes. The project will collect massive multi-modal online feeds from metropolitan information infrastructure to model dynamic behaviors of transportation systems, and then utilize massive micro-level trip information to apply fine-grained real-time control to handle rapid changes in dynamic metropolitan environments. General principles and design methodologies will be designed to build multi-modal, integrated urban transportation systems. These research discoveries will be applied toward commercial applications. Long-term deployment problem of bike stations will be addressed, especially in the low-income districts, to provide suggestions on the station deployment and assessment for specific deployment plans. The project also solves the short-term bike maintenance issue to balance the usage of shared bikes to prevent quick deterioration of rental bikes, and improve availability of bike rental services in real time. This project will also study fleet-oriented ride sharing systems that decide fares based on real-time supply/demand ratio to handle dynamic metropolitan scenarios. This project will support two Ph.D. students who will receive innovation and technology translation training through close interactions with municipal governments and small-business companies. Such partnerships expedite the adoption of cutting-edge technology, evaluate research solutions in operational environments, and obtain user feedback to trigger further innovations. The project will improve the efficiency of existing transportation systems under sharing economy and ultimately the work would noticeably improve the quality of every-day life in metropolitan areas across the United States.

The project funds the organization of a workshop on "Airborne Networking and Communications". The workshop, colocated with the ACM MobiHoc Symposium, will be held on August 11, 2014, in Philadelphia. The workshop will be the third in a series that started in 2012. Airborne Networking is an emerging area of cyber physical systems - and issues of control, processing, and networking lie at the heart of this field. It is important to build a foundational science to support this area as unmanned vehicles become more prevalent. The workshop brings top researchers in area together to help develop a research agenda and continue to further the field. In addition, strong outreach to undergraduate students for participation is a key component of the NSF funding.

Many safety-critical cyber-physical systems rely on advanced sensing capabilities to react to changing environmental conditions. One such domain is automotive systems. In this domain, a proliferation of advanced sensor technology is being fueled by an expanding range of autonomous capabilities (blind spot warnings, automatic lane-keeping, etc.). The limit of this expansion is full autonomy, which has been demonstrated in various one-off prototypes, but at the expensive of significant hardware over-provisioning that is not tenable for a consumer product. To enable features approaching full autonomy in a commercial vehicle, software infrastructure will be required that enables multiple sensor-processing streams to be multiplexed onto a common hardware platform at reasonable cost. This project is directed at the development of such infrastructure. The desired infrastructure will be developed by focusing on a particularly compelling challenge problem: enabling cost-effective driver-assist and autonomous-control automotive features that utilize vision-based sensing through cameras. This problem will be studied by (i) examining numerous multicore-based hardware configurations at various fixed price points based on realistic automotive use cases, and by (ii) characterizing the range of vision-based workloads that can be feasibly supported using the software infrastructure to be developed. The research to be conducted will be a collaboration involving academic researchers at UNC and engineers at General Motors Research. The collaborative nature of this effort increases the likelihood that the results obtained will have real impact in the U.S. automotive industry. Additionally, this project is expected to produce new open-source software and tools, new course content, public outreach through participation in UNC's demo program, and lectures and seminars by the investigators at national and international forums.

One of the challenges for the future cyber-physical systems is the exploration of large design spaces. Evolutionary algorithms (EAs), which embody a simplified computational model of the mutation and selection mechanisms of natural evolution, are known to be effective for design optimization. However, the traditional formulations are limited to choosing values for a predetermined set of parameters within a given fixed architecture. This project explores techniques, based on the idea of hidden genes, which enable EAs to select a variable number of components, thereby expanding the explored design space to include selection of a system's architecture. Hidden genetic optimization algorithms have a broad range of potential applications in cyber-physical systems, including automated construction systems, transportation systems, micro-grid systems, and space systems. The project integrates education with research by involving students ranging from high school through graduate school in activities commensurate with their skills, and promotes dissemination of the research results through open source distribution of algorithm implementation code and participation in the worldwide Global Trajectory Optimization Competition. Instead of using a single layer of coding to represent the variables of the system in current EAs, this project investigates adding a second layer of coding to enable hiding some of the variables, as needed, during the search for the optimal system's architecture. This genetic hiding concept is found in nature and provides a natural way of handling system architectures covering a range of different sizes in the design space. In addition, the standard mutation and selection operations in EAs will be replaced by new operations that are intended to extract the full potential of the hidden gene model. Specific applications include space mission design, microgrid optimization, and traffic network signal coordinated planning.

Securing critical networked cyber-physical systems (NCPSs) such as the power grid or transportation systems has emerged as a major national and global priority. The networked nature of such systems renders them vulnerable to a range of attacks both in cyber and physical domains as corroborated by recent threats such as the Stuxnet worm. Developing security mechanisms for such NCPSs significantly differs from traditional networked systems due to interdependence between cyber and physical subsystems (with attacks originating from either subsystem), possible cooperation between attackers or defenders, and the presence of human decision makers in the loop. The main goal of this research is to develop the necessary science and engineering tools for designing NCPS security solutions that are applicable to a broad range of application domains. This project will develop a multidisciplinary framework that weaves together principles from cybersecurity, control theory, networking and criminology. The framework will include novel security mechanisms for NCPSs founded on solid control-theoretic and related notions, analytical tools that allow incorporation of bounded human rationality in NCPS security, and experiments with real-world attack scenarios. A newly built cross-institutional NCPS simulator will be used to evaluate the proposed mechanisms in realistic environments. This research transcends specific cyber-physical systems domains and provides the necessary tools to building secure and trustworthy NCPSs. The broader impacts include a new infrastructure for NCPS research and education, training of students, new courses, and outreach events focused on under-represented student groups

Title: CPS: Breakthrough: A Mathematical Theory of Cyber-Physical Systems The fundamental challenge in cyber-physical systems is the confluence of distinct scientific and engineering models, methods, and tools for cyber and physical systems. Cyber systems are primarily about processing information. Physical systems are primarily about structure and dynamics, the evolution of state in time. This project develops a mathematical theory of cyber-physical systems that provides a formal interface between the cyber and the physical. The intellectual merits of the project are a solid basis for the modeling and design as well as the implementation and verification of cyber-physical systems, and a fruitful connection of the nascent discipline of cyber-physical-systems engineering with standard mathematical practice. The project's broader significance and importance are providing a sound foundation by which cyber-physical system technologies can be assessed, and enabling the discipline of cyber-physical-system engineering to evolve into a mature and durable field of study. The project builds on the theory of generalized ultrametric semilattices and the fixed-point theory of strictly contracting functions on generalized ultrametric semilattices to develop a cyber-physical domain theory, providing a firm mathematical footing for arbitrary composition and higher-order behavior, formulating the right notion of convergence and continuity for cyber-physical computation, and developing a notion of approximation and effectiveness that allows for a two-way connection between the abstractions of the theory and the realizations of practice. It further applies the theory to a wide range of classic problems of modeling and simulating mixed discrete and continuous phenomena, and extends it to embrace the discrete interventions of a cyber subsystem on its physical counterpart in a cyber-physical system. It also investigates the practical implications of the theory for the implementation and verification of cyber-physical systems by reexamining currently used timed models of computation through the prism of the theory, exploring the extension of programming languages with temporal constructs that are complete over the theoretical abstractions, and integrating the theory in automated and interactive theorem provers to supplement existing model-checking methods that might succumb to the scale of cyber-physical systems.

Despite many advances in vehicle automation, much remains to be done: the best autonomous vehicle today still lags behind human drivers, and connected vehicle (V2V) and infrastructure (V2I) standards are only just emerging. In order for such cyber-physical systems to fully realize their potential, they must be capable of exploiting one of the richest and most complex abilities of humans, which we take for granted: seeing and understanding the visual world. If automated vehicles had this ability, they could drive more intelligently, and share information about road and environment conditions, events, and anomalies to improve situational awareness and safety for other automated vehicles as well as human drivers. That is the goal of this project, to achieve a synergy between computer vision, machine learning and cyber-physical systems that leads to a safer, cheaper and smarter transportation sector, and which has potential applications to other sectors including agriculture, food quality control and environment monitoring. To achieve this goal, this project brings together expertise in computer vision, sensing, embedded computing, machine learning, big data analytics and sensor networks to develop an integrated edge-cloud architecture for (1) "anytime scene understanding" to unify diverse scene understanding methods in computer vision, and (2) "cooperative scene understanding" that leverages vehicle-to-vehicle and vehicle-to-infrastructure protocols to coordinate with multiple systems, while (3) emphasizing how security and privacy should be managed at scale without impacting overall quality-of-service. This architecture can be used for autonomous driving and driver-assist systems, and can be embedded within infrastructure (digital signs, traffic lights) to avoid traffic congestion, reduce risk of pile-ups and improve situational awareness. Validation and transition of the research to practice are through integration within City of Pittsburgh public works department vehicles, Carnegie Mellon University NAVLAB autonomous vehicles, and across the smart road infrastructure corridor under development in Pittsburgh. The project also includes activities to foster development of a new cyber-physical systems workforce, though involvement of students in the research, co-taught multi-disciplinary courses, and co-organized workshops.