Cyber-physical systems employed in transportation, security and manufacturing applications rely on a wide variety of sensors for prediction and control. In many of these systems, acquisition of information requires the deployment and activation of physical sensors, which can result in increased expense or delay. A fundamental aspect of these systems is that they must seek information intelligently in order to support their mission, and must determine the optimal tradeoffs as to the cost of physical measurements versus the improvement in information. A recent explosion in sensor and UAV technology has led to new capabilities for controlling the nature and mobility of sensing actions by changing excitation levels, position, orientation, sensitivity, and similar parameters. This has in turn created substantial challenges to develop cyber-physical systems that can effectively exploit the degrees of freedom in selecting where and how to sense the environment. These challenges include high-dimensionality of observations and the associated "curse of dimensionality", non-trivial relationships between the observations and the latent variables, poor understanding of models relating the nature of potential sensing actions and the corresponding value of the collected information, and lack of sufficient training data from which to learn these models. Intellectual Merit: The proposed research includes: (1) data-driven stochastic control theory for intelligent sensing in cyber-physical systems that incorporates costs/delays/risks and accounts for scenarios where models for sensing, decision-making, and prediction are unavailable or poorly understood. (2) Validation of control methods on a UAV sensor network in the real world domain of archaeological surveying. Broader Impacts: The proposed effort includes: (a) Outreach: planned efforts for encouraging participation of women and under-represented groups; (b) Societal impact: research will lead to novel concepts in environmental monitoring, traffic surveillance, and security applications. (c) Multi- disciplinary activities: Impacting existing knowledge in cyber-physical systems, sensor management, and statistical learning. Research findings will be disseminated through conferences presentations, departmental seminars, journal papers, workshops and special sessions at IEEE CDC and RSS; (d) Curriculum development through new graduate level courses and course projects.

This cross-disciplinary project brings together a team of engineering and computer science researchers to create, validate, and demonstrate the value of new techniques for ensuring that systems composed of combinations of hardware, software, and humans are designed to operate in a truly synergistic and safe fashion. One notable and increasingly common feature of these "Cyber-Physical-Human" (CPH) systems is that the responsibility for safe operation and performance is typically shared by increasingly sophisticated automation in the form of hardware and software, and humans who direct and oversee the behavior of automation yet may need to intervene to take over manual or shared system control when unexpected environmental situations or hardware or software failures occur. The ultimate goal is to achieve levels of safety and performance in system operation that exceed the levels attainable by either skilled human operators or completely autonomous systems acting alone. To do so, the research team will draw upon their expertise in the design of robust, fault-tolerant control systems, in the design of complexity-reduction architectures for software verification, and in human factors techniques for cognitive modeling to assure high levels of human situation awareness through effective interface design. By doing so, the safety, cost and performance benefits of increasingly sophisticated automation can be achieved without the frequently observed safety risks caused by automation creating greater distance between human operators and system operation. The techniques will be iteratively created and empirically evaluated using experimentation in human-in-the-loop simulations, including a medium-fidelity aircraft and flight simulator and a simulation of assistive automation in a medical context. More broadly, this research is expected to impact and inform the engineering of future CPH systems generally, for all industries and systems characterized by an increasing use of hardware and software automation directed and overseen by humans who provide an additional layer of safety in expected situations, Examples include highway and automotive automation, aerospace and air traffic control automation, semi-automated process control systems, and the many forms of automated systems and devices increasingly being used in medical contexts, such as the ICU and operating room. This research is also expected to inform government and industry efforts to provide safety certification criteria for the technologies used in CPH systems, and to educate a next generation of students trained in the cross-disciplinary skills and abilities needed to engineer the CPH systems of the future. The investigators will organize industry, academic, and government workshops to disseminate results and mentor students who are members of underrepresented groups through the course of this research project.

Processors in cyber-physical systems are increasingly being used in applications where they must operate in harsh ambient conditions and a computational workload which can lead to high chip temperatures. Examples include cars, robots, aircraft and spacecraft. High operating temperatures accelerate the aging of the chips, thus increasing transient and permanent failure rates. Current ways to deal with this mostly turn off the processor core or drastically slow it down when some part of it is seen to exceed a given temperature threshold. However, this pass/fail approach ignores the fact that (a) processors experience accelerated aging due to high temperatures, even if these are below the threshold, and (b) while deadlines are a constraint for real-time tasks to keep the controlled plant in the allowed state space, the actual controller response times that will increase if the voltage or frequency is lowered (to cool down the chip) are what determine the controlled plant performance. Existing approaches also fail to exploit the tradeoff between controller reliability (affected by its temperature history) and the performance of the plant. This project addresses these issues. Load-shaping algorithms are being devised to manage thermal stresses while ensuring appropriate levels of control quality. Such actions include task migration, changing execution speed, selecting an alternative algorithm or software implementation of control functions, and terminating prematurely optional portions of iterative tasks. Validation platforms for this project include automobiles and unmanned aerial vehicles. These platforms have been chosen based on both their importance to society and the significant technical challenges they pose. With CPS becoming ever more important in our lives and businesses, this project which will make CPS controllers more reliable and/or economical has broad potential social and economic impacts. Collaboration with General Motors promotes transition of the new technology to industry. The project includes activities to introduce students to thermal control in computing, in courses spanning high-school, undergraduate and graduate curricula.

Processors in cyber-physical systems are increasingly being used in applications where they must operate in harsh ambient conditions and a computational workload which can lead to high chip temperatures. Examples include cars, robots, aircraft and spacecraft. High operating temperatures accelerate the aging of the chips, thus increasing transient and permanent failure rates. Current ways to deal with this mostly turn off the processor core or drastically slow it down when some part of it is seen to exceed a given temperature threshold. However, this pass/fail approach ignores the fact that (a) processors experience accelerated aging due to high temperatures, even if these are below the threshold, and (b) while deadlines are a constraint for real-time tasks to keep the controlled plant in the allowed state space, the actual controller response times that will increase if the voltage or frequency is lowered (to cool down the chip) are what determine the controlled plant performance. Existing approaches also fail to exploit the tradeoff between controller reliability (affected by its temperature history) and the performance of the plant. This project addresses these issues. Load-shaping algorithms are being devised to manage thermal stresses while ensuring appropriate levels of control quality. Such actions include task migration, changing execution speed, selecting an alternative algorithm or software implementation of control functions, and terminating prematurely optional portions of iterative tasks. Validation platforms for this project include automobiles and unmanned aerial vehicles. These platforms have been chosen based on both their importance to society and the significant technical challenges they pose. With CPS becoming ever more important in our lives and businesses, this project which will make CPS controllers more reliable and/or economical has broad potential social and economic impacts. Collaboration with General Motors promotes transition of the new technology to industry. The project includes activities to introduce students to thermal control in computing, in courses spanning high-school, undergraduate and graduate curricula.

The objective of this project is to research tools to manage uncertainty in the design and certification process of safety-critical aviation systems. The research focuses on three innovative ideas to support this objective. First, probabilistic techniques will be introduced to specify system-level requirements and bound the performance of dynamical components. These will reduce the design costs associated with complex aviation systems consisting of tightly integrated components produced by many independent engineering organizations. Second, a framework will be created for developing software components that use probabilistic execution to model and manage the risk of software failure. These techniques will make software more robust, lower the cost of validating code changes, and allow software quality to be integrated smoothly into overall system-level analysis. Third, techniques from Extreme Value Theory will be applied to develop adaptive verification and validation procedures. This will enable early introduction of new and advanced aviation systems. These systems will initially have restricted capabilities, but these restrictions will be gradually relaxed as justified by continual logging of data from in-service products. The three main research aims will lead to a significant reduction in the costs and time required for fielding new aviation systems. This will enable, for example, the safe and rapid implementation of next generation air traffic control systems that have the potential of tripling airspace capacity with no reduction in safety. The proposed methods are also applicable to other complex systems including smart power grids and automated highways. Integrated into the research is an education plan for developing a highly skilled workforce capable of designing safety critical systems. This plan centers around two main activities: (a) creation of undergraduate labs focusing on safety-critical systems, and (b) integration of safety-critical concepts into a national robotic snowplow competition. These activities will provide inspirational, real-world applications to motivate student learning.

Effective engineering of complex devices often depends critically on the ability to encapsulate responsibility for tasks into modular agents and ensure those agents communicate with one another in well-defined and easily observable ways. When such conditions are followed, it becomes possible to detect where problems lie so they can be corrected. It also becomes possible to optimize the agents and their communications to improve performance. Cyber-physical systems (like robots, self-piloting aircraft, etc.) modify themselves to improve performance break those conditions in that some agent modules negotiate their own communications and decide their own actions, sometimes taking advantage of the physics of the world in ways we did not anticipate. This renders difficult application of standard engineering tools to accomplish critical fault diagnosis and design optimization. This project will produce analysis methods address the specific needs of cyber-physical systems that, by their natures, break the rules of convention. We will apply these new methods to the design and analysis of self-improving controllers for flapping-wing micro air vehicles. This work will provide advances in both model-checking related formal design methodologies and in module-based self-adaptive control in computationally resource constrained cyber-physical systems. The formal methods advances will significantly expand our ability to properly design and verify systems that tightly couple computation, sensors, and actuators. The specific test application addressed is significant to a number of nationally important security and defense efforts and will directly impact identified national priorities.

Effective engineering of complex devices often depends critically on the ability to encapsulate responsibility for tasks into modular agents and ensure those agents communicate with one another in well-defined and easily observable ways. When such conditions are followed, it becomes possible to detect where problems lie so they can be corrected. It also becomes possible to optimize the agents and their communications to improve performance. Cyber-physical systems (like robots, self-piloting aircraft, etc.) modify themselves to improve performance break those conditions in that some agent modules negotiate their own communications and decide their own actions, sometimes taking advantage of the physics of the world in ways we did not anticipate. This renders difficult application of standard engineering tools to accomplish critical fault diagnosis and design optimization. This project will produce analysis methods address the specific needs of cyber-physical systems that, by their natures, break the rules of convention. We will apply these new methods to the design and analysis of self-improving controllers for flapping-wing micro air vehicles. This work will provide advances in both model-checking related formal design methodologies and in module-based self-adaptive control in computationally resource constrained cyber-physical systems. The formal methods advances will significantly expand our ability to properly design and verify systems that tightly couple computation, sensors, and actuators. The specific test application addressed is significant to a number of nationally important security and defense efforts and will directly impact identified national priorities.