Harnessing wind energy is one of the pressing challenges of our time. The scale, complexity, and robustness of wind power systems present compelling cyber-physical system design issues. Leveraging the physical infrastructure at Purdue, this project aims to develop comprehensive computational infrastructure for distributed real-time control. In contrast to traditional efforts that focus on programming-in-the-small, this project emphasizes programmability, robustness, longevity, and assurance of integrated wind farms. The design of the proposed computational infrastructure is motivated by, and validated on, complex cyber-physical interactions underlying Wind Power Engineering. There are currently no high-level tools for expressing coordinated behavior of wind farms. Using the proposed cyber-physical system, the project aims to validate the thesis that integrated control techniques can significantly improve performance, reduce downtime, improve predictability of maintenance, and enhance safety in operational environments. The project has significant broader impact. Wind energy in the US is the fastest growing source of clean, renewable domestically produced energy. Improvements in productivity and longevity of this clean energy source, even by a few percentage points will have significant impact on the overall energy landscape and decision-making. Mitigating failures and enhancing safety will go a long way towards shaping popular perceptions of wind farms -- accelerating broader acceptance within local communities. Given the relative infancy of "smart" wind farms, the potential of the project cannot be overstated.

Three emerging technologies provide unique opportunities for denser cities throughout the developed world: vehicle sharing, electric vehicles, and autonomous systems. Bringing these technologies close together can help enable joint mobility-on-demand and urban-logistics services. This project focuses on the co-development of design and algorithms to enable new concepts that will serve this purpose. The Persuasive Electric Vehicle (PEV) is a tricycle navigating in the bike lanes. The PEV can autonomously drive itself to its next customer; it can also deliver packages to its customers who order goods online. On the algorithmic front, the project will investigate (i) provably-safe algorithms for autonomous navigation in bike lanes, and (ii) algorithms for high-performance routing and rebalancing for joint mobility on demand and urban logistics. On the design front, the project will investigate (i) the vehicle-level designs that can best embrace the relevant CPS technologies, and (ii) the system-level designs and urban planning practices that can help enable the PEV concept. The PIs will collaborate with the City of Boston and participate in the Global City Teams Challenge, where they will demonstrate the PEV concept and its potential impact on future smart cities.

This project exploits an early concept of a flexible, low-cost, and drone-carried broadband long-distance communication infrastructure and investigates its capability for immediate smart-city application in emergency response. This effort is to support the Smart Emergency Response System (SERS) cluster to participate in the Global City Teams Challenge. This project will have an immediate impact in firefighting and other smart-city emergency response applications by quickly deploying a broadband communication infrastructure, thus improving the efficiency of first responders and saving lives. This communication infrastructure expands the capability of individual drones and enables broad new multi-drone applications for smart cities and has the potential to create new businesses and job markets. This interdisciplinary project addresses the following technology issues: 1) development of cyber-physical systems (CPS) technology that enables robust long-range drone-to-drone communication infrastructure; 2) practical drone system design and performance evaluation for WiFi provision; and 3) a systematic investigation of its capability to address smart-city emergency response needs, through both analysis and participation in fire-fighting exercises, as a case study. The project team includes an academic institution, technology companies and government planners, each of whom provides complementary expertise and perspectives that are crucial to the success of the project. The project also provides exciting interdisciplinary training opportunities for students and the community to learn CPS technologies and the Global City Teams Challenge efforts.

This project will work with national and international medical and disaster professionals to extract formal use cases for ground, aerial, and marine robots for medical response and humanitarian relief to the Ebola (and future) epidemics. A set of detailed use cases is urgently needed to meet the challenges posed by the epidemic and to prepare robotics for assisting with future epidemics. The robotics community cannot provide robots without understanding the needs and engineering mistakes or mismatches will both be financially costly and delay the delivery of effective solutions. This is a rare opportunity to work with responders as they plan for a deployment of more than 3,000 troops plus Centers for Disease Control workers, and a possibly greater number of volunteers through non-governmental organizations such as Doctors Without Borders. The project outcomes will allow robotics companies to confidently pre-position/re-position products and to incorporate the findings into R&D investment strategies. The categorization of problems will guide academia in future research and to use as motivating class projects. The effective use of robots will provide responders with tools for the short term and will provide achievable expectations of robotics technology in general. There is no comprehensive statement of the missions that robots can be used for during a medical event and general mission descriptions (e.g. we need a robot to transport bodies) do not capture the design constraints on a robot. Prior work has shown that not understanding the operational envelope, work domain, and culture results in overly expensive robots that cannot be adopted. Robotics has not been considered by health professionals for the entire space of a medical event (hospitals, field medicine, logistics, security from riots), nor has the disaster or medical robotics communities been engaged with epidemics. This project will provide the fundamental understanding of how robots can be used for medical disasters and will design a formal process for projecting robotics requirements. It will benefit safety security and rescue robotics by expanding research from meteorological, geological, and man-made disasters to medical disasters and surgical robotics and telerobotics by pushing the boundaries of how robots are used for biosafety event.

Human-in-the-loop control strategies in which the user performs a task better, and feels more confident to do so, is an important area of research for cyber-physical systems. Humans are very adept at learning to control complex systems, particularly those with non-intuitive kinematic constraints (e.g., cars, bicycles, wheelchairs, steerable needles). With the advent of cyber-physical systems, (physical systems integrated with cyber control layer), human control is no longer constrained to system inputs. Users can also control system outputs through a number of different teleoperation mappings. Given all this flexibility, what is the most intuitive way for a human user to control an arbitrary system and how is intuitiveness quantified? The project focuses on human-in-the-loop control for medical needles, which steer with bicycle-like kinematics. These needles could be used in a variety of medical interventions including tissue biopsy, tumor ablation, abscess drainage, and local drug delivery. We have explored a variety of teleoperation mappings for human control of these steerable needles; yet, we have found inconsistencies between objective performance metrics (e.g., task time and error), and post-experimental surveys on comfort or ease-of use. Users occasionally report a preference for control mappings, which objectively degrade performance, and vice versa. It is important to measure the real-time engagement of the user with the physical system in order to capture the nuances of how different control mappings affect physical effort, mental workload, distraction, drowsiness, and emotional response. Physiological sensors such as electroencephalography (EEG), galvanic skin response (GSR), and electromyography (EMG), can be used to provide these real-time measurements and to quantitatively classify the intuitiveness of new teleoperation algorithms. Broader Impacts: Intuitive and natural human-in-the-loop control interfaces will improve human health and well being, through applications in surgery and rehabilitation. The results of this study will be disseminated publicly on the investigator's laboratory website, a conference workshop, and a new medical robotics seminar to be held jointly between UT Dallas and UT Southwestern Medical Center. Outreach activities, lab tours, and mentoring of underrepresented students at all levels, will broaden participation in STEM. Additionally, the proximity of the investigator?s hospital-based lab to medical professionals will engage non-engineers in design and innovation

This proposal will establish a framework for developing distributed Cyber-Physical Systems operating in a Networked Control Systems (NCS) environment. Specific attention is focused on an application where the computational, and communication challenges are unique due to the sheer size of the physical system, and communications between system elements include potential for significant losses and delays. An example of this is the power grid which includes large-scale deployment of distributed and networked Phasor Measurement Units (PMUs) and wind energy resources. Although, much has been done to model and analyze the impact of data dropouts and delay in NCS at a theoretical level, their impact on the behavior of cyber physical systems has received little attention. As a result much of the past research done on the `smart grid' has oversimplified the `physical' portion of the model, thereby overlooking key computational challenges lying at the heart of the dimensionality of the model and the heterogeneity in the dynamics of the grid. A clear gap has remained in understanding the implications of uncertainties in NCS (e.g. bandwidth limitations, packet dropout, packet disorientation, latency, signal loss, etc.) cross-coupled with the uncertainties in a large power grid with wind farms (e.g. variability in wind power, fault and nonlinearity, change in topology etc.) on the reliable operation of the grid. To address these challenges, this project will, for the first time, develop a modeling framework for discovering hitherto unknown interactions through co-simulation of NCS, distributed computing, and a large power grid included distributed wind generation resources. Most importantly, it addresses challenges in distributed computation through frequency domain abstractions and proposes two novel techniques in grid stabilization during packet dropout. The broader impact lies in providing deeper understanding of the impact of delays and dropouts in the Smart Grid. This will enable a better utilization of energy transmission assets and improve integration of renewable energy sources. The project will facilitate participation of women in STEM disciplines, and will include outreach with local Native American tribal community colleges This project will develop fundamental understanding of impact of network delays and drops using an approach that is applicable to a variety of CPS. It will enable transformative Wide-Areas Measurement Systems research for the smart grid through modeling adequacy studies of a representative sub-transient model of the grid along with the representation of packet drop in the communication network by a Gilbert model. Most importantly, fundamental concepts of frequency domain abstraction including balanced truncation and optimal Hankel-norm approximation are proposed to significantly reduce the burden of distributed computing. Finally, a novel `reduced copy' approach and a `modified Kalman filtering' approach are proposed to address the problem of grid stabilization using wind farm controls when packet drop is encountered.

This project advances the scientific knowledge on design methods for improving the resilience of civil infrastructures to disruptions. To improve resilience, critical services in civil infrastructure sectors must utilize new diagnostic tools and control algorithms that ensure survivability in the presence of both security attacks and random faults, and also include the models of incentives of human decision makers in the design process. This project will develop a practical design toolkit and platform to enable the integration of resiliency-improving control tools and incentive schemes for Cyber-Physical Systems (CPS) deployed in civil infrastructures. Theory and algorithms will be applied to assess resiliency levels, select strategies to improve performance, and provide reliability and security guarantees for sector-specific CPS functionalities in water, electricity distribution and transportation infrastructures. The main focus is on resilient design of network control functionalities to address problems of incident response, demand management, and supply uncertainties. More broadly, the knowledge and tools from this project will influence CPS designs in water, transport, and energy sectors, and also be applicable to other systems such as supply-chains for food, oil and gas. The proposed platform will be used to develop case studies, test implementations, and design projects for supporting education and outreach activities. Current CPS deployments lack integrated components designed to survive in uncertain environments subject to random events and the actions of strategic entities. The toolkit (i) models the propagation of disruptions due to failure of cyber-physical components, (ii) detects and responds to both local and network-level failures, and (iii) designs incentive schemes that improve aggregate levels of public good (e.g., decongestion, security), while accounting for network interdependencies and private information among strategic entities. The validation approach uses real-world data collected from public sources, test cases developed by domain experts, and simulation software. These tools are integrated to provide a multi-layer design platform, which explores the design space to synthesize solutions that meet resiliency specifications. The platform ensures that synthesized implementations meet functionality requirements, and also estimates the performance guarantees necessary for CPS resilience. This modeling, validation, exploration, and synthesis approach provides a scientific basis for resilience engineering. It supports CPS education by providing a platform and structured workflow for future engineers to approach and appreciate implementation realities and socio-technical constraints.

Cyber-physical critical infrastructures integrate networks of computational and physical processes to provide the society with essential services. The power grid, in particular, is a vast and interconnected cyber-physical network for delivering electricity from generation plants to end-point consumers. Protecting power grid critical infrastructures is a vital necessity because the failure of these systems would have a debilitating impact on economic security and public health and safety. However, several recent large-scale outages and the significant increase in the number of major attacks over the past four years confirm the insufficiency of the current protection solutions for these systems. Existing tedious manual tolerance procedures cannot protect those grids against sophisticated attacks. Additionally, use of purely-cyber security solutions for power grid resiliency is not sufficient because they ignore the cyber-physical interdependencies, power-side sensor measurements, and the possibility of countermeasures in power infrastructures. The objective of this research is to investigate fundamental problems in cyber-physical tolerance and develop an integrated set of mathematically rigorous and real-world deployable capabilities, resulting in a system that can model, analyze, predict, and tolerate complex security incidents in computing, physical, or communication assets in a near-real-time manner. The proposed research will provide system administrators and power grid operators with scalable and online integrated cyber-physical monitoring and incident response capabilities through keeping track of cyber-physical infrastructure's dynamic evolution caused by distributed security incidents, optimal proactive response and recovery countermeasures and adaptive preparation for potential future security incidents. The proposed research will facilitate trustworthy operation of next-generation complex and large-scale power grids. The research outcomes will be integrated into educational and knowledge transfer initiatives that involves implementation of curricular activities, innovative learning game development, university workshops, and hands-on K-12 summer camps and academic-year high-school courses, as well as Industry technology transfer efforts to develop a workforce with the capability to reason across multiple disciplines. Through holistic consideration of both cyber and physical factors under adversarial situations, this fundamental work will be applicable to other cyber-physical domains and can transform the way people approach the problem of cyber-physical security.

The electric power grid, a cyber-physical system (CPS), faces an alarmingly high risk of catastrophic damage from cyber-attacks. However, modeling cyber-attacks, evaluating consequences, and developing appropriate countermeasures require a detailed, realistic, and tractable model of electric power CPS operations. The primary barrier is the lack of access to models for the complex legacy proprietary systems upon which the electric power grid has relied for decades. This project aims to overcome these challenges with the development of an attack-verifying (verifiable) software framework that will capture the electric power system operations in adequate detail. Cyber threats will be verified using this framework through a combination of sound theoretical methods and an open-source commercial simulation engine accessible via a unique transition to practice (TTP) option. This research focuses on four fundamental and related thrusts: (i) identifying classes of cyber-attacks with quantifiable physical consequences and developing detection-based countermeasures; (ii) identifying communication attacks on distributed grid operations and developing information-sharing countermeasures; (iii) developing a verifiable software framework that models the spatio-temporal operations of the electric grid in tandem with thrusts (i) and (ii) to verify attack models, evaluate countermeasures, and develop new resiliency protocols; and (iv) a TTP option, in collaboration with industry-leading experts from IncSys and PowerData, to develop commercial grade open source power simulation software packages to integrate and test the attacks and countermeasures of Thrusts (i) through (iii) as well as develop workforce training curriculum for North American Electric Reliability Council (NERC) certification. This research also includes engagement with K-12 students via the Arizona Science Laboratory program.

All cyber-physical systems (CPS) depend on properly calibrated sensors to sense the surrounding environment. Unfortunately, the current state of the art is that calibration is often a manual and expensive operation; moreover, many types of sensors, especially economical ones, must be recalibrated often. This is typically costly, performed in a lab environment, requiring that sensors be removed from service. MetaSense will reduce the cost and management burden of calibrating sensors. The basic idea is that if two sensors are co-located, then they should report similar values; if they do not, the least-recently-calibrated sensor is suspect. Building on this idea, this project will provide an autonomous system and a set of algorithms that will automate the detection of calibration issues and preform recalibration of sensors in the field, removing the need to take sensors offline and send them to a laboratory for calibration. The outcome of this project will transform the way sensors are engineered and deployed, increasing the scale of sensor network deployment. This in turn will increase the availability of environmental data for research, medical, personal, and business use. MetaSense researchers will leverage this new data to provide early warning for factors that could negatively affect health. In addition, graduate student engagement in the research will help to maintain the STEM pipeline. This project will leverage large networks of mobile sensors connected to the cloud. The cloud will enable using large data repositories and computational power to cross-reference data from different sensors and detect loss of calibration. The theory of calibration will go beyond classical models for computation and physics of CPS. The project will combine big data, machine learning, and analysis of the physics of sensors to calculate two factors that will be used in the calibration. First, MetaSense researchers will identify measurement transformations that, applied in software after the data collection, will generate calibrated results. Second, the researchers will compute the input for an on-board signal-conditioning circuit that will enable improving the sensitivity of the physical measurement. The project will contribute research results in multiple disciplines. In the field of software engineering, the project will contribute a new theory of service reconfiguration that will support new architecture and workflow languages. New technologies are needed because the recalibration will happen when the machine learning algorithms discover calibration errors, after the data has already been collected and processed. These technologies will support modifying not only the raw data in the database by applying new calibration corrections, but also the results of calculations that used the data. In the field of machine learning, the project will provide new algorithms for dealing with spatiotemporal maps of noisy sensor readings. In particular, the algorithms will work with Gaussian processes and the results of the research will provide more meaningful confidence intervals for these processes, substantially increasing the effectiveness of MetaSense models compared to the current state of the art. In the field of pervasive computing, the project will build on the existing techniques for context-aware sensing to increase the amount of information available to the machine learning algorithms for inferring calibration parameters. Adding information about the sensing context is paramount to achieve correct calibration results. For example, a sensor that measures air pollution inside a car on a highway will get very different readings if the car window is open or closed. Finally, the project will contribute innovations in sensor calibration hardware. Here, the project will contribute innovative signal-conditioning circuits that will interact with the cloud system and receive remote calibration parameters identified by the machine learning algorithms. This will be a substantial advance over current circuits based on simple feedback loops because it will have to account for the cloud and machine learning algorithms in the loop and will have to perform this more complex calibration with power and bandwidth constraints. Inclusion of graduate students in the research helps to maintain the STEM pipeline.