Advances in the effective integration of networked information systems, sensing and communication devices, data sources, decision making, and physical infrastructure are transforming society, allowing cities and communities to surmount deeply interlocking physical, social, behavioral, economic, and infrastructural challenges. These novel socio-technical approaches enable increased understanding of how to intelligently and effectively design, adapt, and manage Smart and Connected Communities (S&CC). Successful S&CC solutions demand demonstration of marked improvement (quantifiable evidence) of the stakeholder experience - whether in personal quality of life, community and environmental health, social well-being, educational achievement, or overall economic growth and stability. Research in S&CC must pursue transformative discoveries through a long-term research agenda that also includes innovation off-ramps along the way. In order to advance research in this area, we are convening a community visioning workshop focused on Smart and Connected Communities. The purpose of the workshop is to engage academic researchers, industry partners, and municipal leaders in detailed discussions on research gaps, practical needs, and priorities for enabling the smart and connected communities of the future. The ultimate goal is to create a research agenda to achieve the S&CC vision. A workshop report will capture major research and implementation challenges, promising approaches, and potential pilot solutions. The report will be made available to all interested parties regardless of their participation. Workshop topics cover a broad waterfront of challenges faced by communities today spanning large and small cities, metropolitan areas, and rural regions. A key emphasis is the integration of social and technical perspectives to develop long term research agenda that can address the needs of communities that are socially and economically heterogeneous.

Inadequate system understanding and inadequate situational awareness have caused large-scale power outages in the past. With the increased reliance on variable energy supply sources, system understanding and situational awareness of a complex energy system become more challenging. This project leverages the power of big data analytics to directly improve system understanding and situational awareness. The research provides the methodology for detecting anomalous events in real-time, and therefore allow control centers to take appropriate control actions before minor events develop into major blackouts. The significance for the society and for the power industry is profound. Energy providers will be able to prevent large-scale power outages and reduce revenue losses, and customers will benefit from reliable energy delivery with service guarantees. Students, including women and underrepresented groups, will be trained for the future workforce in this area. The project includes four major thrusts: 1) real-time anomaly detection from measurement data; 2) real-time event diagnosis and interpretation of changes in the state of the network; 3) real-time optimal control of the power grid; 4) scientific foundations underpinning cyber-physical systems. The major outcome of this project is practical solutions to event or fault detection and diagnosis in the power grid, as well as prediction and prevention of large-scale power outages.

Due to their increasing use by civil and federal authorities and vast commercial and amateur applications, Unmanned Aerial Systems (UAS) will be introduced into the National Air Space (NAS); the question is only how this can be done safely. Today, NASA and the FAA are designing a new, (NextGen) automated air traffic control system for all aircraft, manned or unmanned. New algorithms and tools will need to be developed to enable computation of the complex questions inherent in designing such a system while proving adherence to rigorous safety standards. Researchers must develop the tools of formal analysis to be able to address the UAS in the NAS problem, reason about UAS integration during the design phase of NextGen, and tie this design to on-board capabilities to provide runtime System Health Management (SHM), ensuring the safety of people and property on the ground. This proposal takes a holistic view and integrates advances in the state of the art from three intertwined perspectives to address safe integration of unmanned systems into the national airspace: from on-board the vehicle, from the environment (NAS), and from the underlying theory enabling their formal analysis. There has been rapid development of new UAS technologies yet few of them are formally mathematically rigorous to the degree needed for FAA safety-critical system certification. This project bridges that gap, integrating new UAS and air traffic control designs with advances in formal analysis. Within the wealth of promising directions for autonomous UAS capabilities, this project fills a unique need, providing a direct synergy between on-board UAS SHM, the NAS environment in which they must operate, and the theoretical foundations common to both of these. This research will help to build a safer NAS with increased capacity for UAS and create broadly impactful capabilities for SHM on-board UAS. Advancements will require theoretical research into more scalable model checking and debugging of safety properties. Safety properties express the sentiment that "something bad does not happen" during any system execution; they represent the vast majority of the requirements for NextGen designs and all requirements researchers can monitor on-board a UAS for system heath management during runtime. This research will tackle new frontiers in embedding health management capabilities on-board UAS. Collaborations with aerospace system designers at the National Aeronautics and Space Administration and tool designers at the Bruno Kessler Foundation will aid real-life utility and technology transfer. Broader impact will be achieved by involving undergraduate students in the design of an open-source, affordable, all-COTS and 3D-printable UAS, which will facilitate flight testing of this project's research advances. An open-UAS design for academia will be useful both for classroom demonstrations and as a research platform. Further impact will be achieved by using this UAS and the research it enables in interactive teaching experiences for K-12, undergraduate, and graduate students and in mentoring outreach specifically targeted at girls achieving in Science, Technology, Engineering and Mathematics (STEM) subjects.

Software-Defined Control (SDC) is a revolutionary methodology for controlling manufacturing systems that uses a global view of the entire manufacturing system, including all of the physical components (machines, robots, and parts to be processed) as well as the cyber components (logic controllers, RFID readers, and networks). As manufacturing systems become more complex and more connected, they become more susceptible to small faults that could cascade into major failures or even cyber-attacks that enter the plant, such as, through the internet. In this project, models of both the cyber and physical components will be used to predict the expected behavior of the manufacturing system. Since the components of the manufacturing system are tightly coupled in both time and space, such a temporal-physical coupling, together with high-fidelity models of the system, allows any fault or attack that changes the behavior of the system to be detected and classified. Once detected and identified, the system will compute new routes for the physical parts through the plant, thus avoiding the affected locations. These new routes will be directly downloaded to the low-level controllers that communicate with the machines and robots, and will keep production operating (albeit at a reduced level), even in the face of an otherwise catastrophic fault. These algorithms will be inspired by the successful approach of Software-Defined Networking. Anomaly detection methods will be developed that can ascertain the difference between the expected (modeled) behavior of the system and the observed behavior (from sensors). Anomalies will be detected both at short time-scales, using high-fidelity models, and longer time-scales, using machine learning and statistical-based methods. The detection and classification of anomalies, whether they be random faults or cyber-attacks, will represent a significant contribution, and enable the re-programming of the control systems (through re-routing the parts) to continue production. The manufacturing industry represents a significant fraction of the US GDP, and each manufacturing plant represents a large capital investment. The ability to keep these plants running in the face of inevitable faults and even malicious attacks can improve productivity -- keeping costs low for both manufacturers and consumers. Importantly, these same algorithms can be used to redefine the production routes (and machine programs) when a new part is introduced, or the desired production volume is changed, to maximize profitability for the manufacturing operation .

Software-Defined Control (SDC) is a revolutionary methodology for controlling manufacturing systems that uses a global view of the entire manufacturing system, including all of the physical components (machines, robots, and parts to be processed) as well as the cyber components (logic controllers, RFID readers, and networks). As manufacturing systems become more complex and more connected, they become more susceptible to small faults that could cascade into major failures or even cyber-attacks that enter the plant, such as, through the internet. In this project, models of both the cyber and physical components will be used to predict the expected behavior of the manufacturing system. Since the components of the manufacturing system are tightly coupled in both time and space, such a temporal-physical coupling, together with high-fidelity models of the system, allows any fault or attack that changes the behavior of the system to be detected and classified. Once detected and identified, the system will compute new routes for the physical parts through the plant, thus avoiding the affected locations. These new routes will be directly downloaded to the low-level controllers that communicate with the machines and robots, and will keep production operating (albeit at a reduced level), even in the face of an otherwise catastrophic fault. These algorithms will be inspired by the successful approach of Software-Defined Networking. Anomaly detection methods will be developed that can ascertain the difference between the expected (modeled) behavior of the system and the observed behavior (from sensors). Anomalies will be detected both at short time-scales, using high-fidelity models, and longer time-scales, using machine learning and statistical-based methods. The detection and classification of anomalies, whether they be random faults or cyber-attacks, will represent a significant contribution, and enable the re-programming of the control systems (through re-routing the parts) to continue production. The manufacturing industry represents a significant fraction of the US GDP, and each manufacturing plant represents a large capital investment. The ability to keep these plants running in the face of inevitable faults and even malicious attacks can improve productivity -- keeping costs low for both manufacturers and consumers. Importantly, these same algorithms can be used to redefine the production routes (and machine programs) when a new part is introduced, or the desired production volume is changed, to maximize profitability for the manufacturing operation .

This Frontier award supports the SONYC project, a smart cities initiative focused on developing a cyber-physical system (CPS) for the monitoring, analysis and mitigation of urban noise pollution. Noise pollution is one of the topmost quality of life issues for urban residents in the U.S. with proven effects on health, education, the economy, and the environment. Yet, most cities lack the resources for continuously monitoring noise and understanding the contribution of individual sources, the tools to analyze patterns of noise pollution at city-scale, and the means to empower city agencies to take effective, data-driven action for noise mitigation. The SONYC project advances novel technological and socio-technical solutions that help address these needs. SONYC includes a distributed network of both sensors and people for large-scale noise monitoring. The sensors use low-cost, low-power technology, and cutting-edge machine listening techniques, to produce calibrated acoustic measurements and recognizing individual sound sources in real time. Citizen science methods are used to help urban residents connect to city agencies and each other, understand their noise footprint, and facilitate reporting and self-regulation. Crucially, SONYC utilizes big data solutions to analyze, retrieve and visualize information from sensors and citizens, creating a comprehensive acoustic model of the city that can be used to identify significant patterns of noise pollution. This data can in turn be used to drive the strategic application of noise code enforcement by city agencies, in a way that optimally reduces noise pollution. The entire system, integrating cyber, physical and social infrastructure, forms a closed loop of continuous sensing, analysis and actuation on the environment. SONYC is an interdisciplinary collaboration between researchers at New York University and Ohio State University. It provides multiple educational opportunities to students at all levels, including an outreach initiative for K-12 STEM education. The project uses New York City as its focal point, involving partnerships with the city's Department of Environmental Protection, Department of Health and Mental Hygiene, the business improvement district of Lower Manhattan, and ARUP, one of the world's leaders in environmental acoustics. SONYC is an innovative and high-impact application of cyber-physical systems to the realm of smart cities, and potentially a catalyst for new CPS research at the intersection of engineering, data science and the social sciences. It provides a blueprint for the mitigation of noise pollution that can be applied to cities in the US and abroad, potentially affecting the quality of life of millions of people.

The recent increase in the variety and usage of wearable sensing systems allows for the continuous monitoring of health and wellness of users. The output of these systems enable individuals to make changes to their personal routines in order to minimize exposures to pollutants and maintain healthy levels of exercise. Furthermore, medical practitioners are using these systems to monitor proper activity levels for rehabilitation purposes and to monitor threatening conditions such as heart arrhythmias. However, there is substantial work to be done to facilitate the processing and interpretation of such information in order to maximize impact. This proposal develops a computational framework that models the complex interactions between physiological and environmental factors contributing to an individual's health. The contributions of this award will facilitate the broad adoption of wearable sensing platforms and innovative analytical tools by individuals and medical practitioners. This award develops methodology for the estimation and prediction of physiological responses and environmental factors, with the objective of enabling users to efficiently change their behavior. To accomplish this objective, the framework will build on tools from statistical analysis, topological data analysis, optimization theory and human behavior analysis. This novel framework will not only develop new formal techniques, but it will also serve as a bridge between these cross-disciplinary fields. In particular, the proposed hierarchical computational framework has the potential of providing a trade-off between accuracy and computational flexibility based on the choice of granularity of the representation. This award will: (1) develop methodology for the concurrent representation of physiological, kinematic and environmental states for inference purposes; (2) develop techniques for mapping representations between different systems to enable information sharing; and (3) develop techniques to maximize the impact on the behavior of individuals by building on the proposed data representation. The algorithm development will be informed by integration of limitations on embedded platforms due to memory, computational and power capabilities, and transmission costs when off-board processing is required. The proposed techniques will empower users and medical practitioners to understand, analyze, and make decisions based on patterns in the data. The outcomes of this project will empower medical practitioners by providing innovative and effective tools for wearable sensing systems which enable efficient pattern identification, data representation and visualization. Besides training students directly working on this project, the data sets and algorithms developed will be incorporated into a new graduate course on computational techniques for physiological and environmental sensing. Undergraduate students will be engaged by participating in data collection experiments, REUs, and local demonstrations. Underrepresented undergraduate student communities will be exposed to the research at the national level by presenting demos at well-known diversity conferences in the STEM fields. Furthermore, K-12 local student communities will be engaged via summer workshops that will be prepared for students and educators.

Equipment operation represents one of the most dangerous tasks on a construction sites and accidents related to such operation often result in death and property damage on the construction site and the surrounding area. Such accidents can also cause considerable delays and disruption, and negatively impact the efficiency of operations. This award will conduct research to improve the safety and efficiency of cranes by integrating advances in robotics, computer vision, and construction management. It will create tools for quick and easy planning of crane operations and incorporate them into a safe and efficient system that can monitor a crane's environment and provide control feedback to the crane and the operator. Resulting gains in safety and efficiency wil reduce fatal and non-fatal crane accidents. Partnerships with industry will also ensure that these advances have a positive impact on construction practice, and can be extended broadly to smart infrastructure, intelligent manufacturing, surveillance, traffic monitoring, and other application areas. The research will involve undergraduates and includes outreach to K-12 students. The work is driven by the hypothesis that the monitoring and control of cranes can be performed autonomously using robotics and computer vision algorithms, and that detailed and continuous monitoring and control feedback can lead to improved planning and simulation of equipment operations. It will particularly focus on developing methods for (a) planning construction operations while accounting for safety hazards through simulation; (b) estimating and providing analytics on the state of the equipment; (c) monitoring equipment surrounding the crane operating environment, including detection of safety hazards, and proximity analysis to dynamic resources including materials, equipment, and workers; (d) controlling crane stability in real-time; and (e) providing feedback to the user and equipment operators in a "transparent cockpit" using visual and haptic cues. It will address the underlying research challenges by improving the efficiency and reliability of planning through failure effects analysis and creating methods for contact state estimation and equilibrium analysis; improving monitoring through model-driven and real-time 3D reconstruction techniques, context-driven object recognition, and forecasting motion trajectories of objects; enhancing reliability of control through dynamic crane models, measures of instability, and algorithms for finding optimal controls; and, finally, improving efficiency of feedback loops through methods for providing visual and haptic cues.

Computation is everywhere. Greeting cards have processors that play songs. Fireworks have processors for precisely timing their detonation. Computers are in engines, monitoring combustion and performance. They are in our homes, hospitals, offices, ovens, planes, trains, and automobiles. These computers, when networked, will form the Internet of Things (IoT). The resulting applications and services have the potential to be even more transformative than the World Wide Web. The security implications are enormous. Internet threats today steal credit cards. Internet threats tomorrow will disable home security systems, flood fields, and disrupt hospitals. The root problem is that these applications consist of software on tiny low-power devices and cloud servers, have difficult networking, and collect sensitive data that deserves strong cryptography, but usually written by developers who have expertise in none of these areas. The goal of the research is to make it possible for two developers to build a complete, secure, Internet of Things applications in three months. The research focuses on four important principles. The first is "distributed model view controller." A developer writes an application as a distributed pipeline of model-view-controller systems. A model specifies what data the application generates and stores, while a new abstraction called a transform specifies how data moves from one model to another. The second is "embedded-gateway-cloud." A common architecture dominates Internet of Things applications. Embedded devices communicate with a gateway over low-power wireless. The gateway processes data and communicates with cloud systems in the broader Internet. Focusing distributed model view controller on this dominant architecture constrains the problem sufficiently to make problems, such as system security, tractable. The third is "end-to-end security." Data emerges encrypted from embedded devices and can only be decrypted by end user applications. Servers can compute on encrypted data, and many parties can collaboratively compute results without learning the input. Analysis of the data processing pipeline allows the system and runtime to assert and verify security properties of the whole application. The final principle is "software-defined hardware." Because designing new embedded device hardware is time consuming, developers rely on general, overkill solutions and ignore the resulting security implications. The data processing pipeline can be compiled into a prototype hardware design and supporting software as well as test cases, diagnostics, and a debugging methodology for a developer to bring up the new device. These principles are grounded in Ravel, a software framework that the team collaborates on, jointly contributes to, and integrates into their courses and curricula on cyberphysical systems.

Cyber-physical systems (CPS) encompass the next generation of computerized control for countless aspects of the physical world and interactions thereof. The typical engineering process for CPS reuses existing designs, models, components, and software from one version to the next. For example, in automotive engineering, it is common to reuse significant portions of existing model-year vehicle designs when developing the next model-year vehicle, and such practices are common across CPS industries, from aerospace to biomedical. While reuse drastically enhances efficiency and productivity, it leads to the possibility of introducing unintended mismatches between subcomponents' specifications. For example, a 2011 US National Highway Traffic Safety Administration (NHTSA) recall of over 1.5 million model-year 2005-2010 vehicles was due to the upgrade of a physical transmission component that was not appropriately addressed in software. A mismatch between cyber and physical specifications may occur when a software or hardware upgrade (in effect, a cyber or physical specification change) is not addressed by an update (in effect, a matching specification change) in the other domain. This research will develop new techniques and software tools to detect automatically if cyber-physical specification mismatches exist, and then mitigate the effects of such mismatches at runtime, with the overall goal to yield more reliable and safer CPS upon which society increasingly depends. The detection and mitigation methods developed will be evaluated in an energy CPS testbed. While the evaluation testbed is in the energy domain, the methods are applicable to other CPS domains such as automotive, aerospace, and biomedical. The educational goals will bridge gaps between computer science and electrical engineering, preparing a diverse set of next-generation CPS engineers by developing education platforms to enhance CPS engineering design and verification skills. The proposed research is to develop new techniques and tools to automatically identify and mitigate the effects of cyber-physical specification mismatches. There are three major research objectives. The first objective is to identify cyber-physical specification mismatches. To identify mismatches, a detection problem will be formalized using the framework of hybrid input/output automata (HIOA). Offline algorithms will be designed to find candidate specifications from models and implementations using static and dynamic analyses, and then identify candidate mismatches. The second objective is to monitor and assure safe CPS upgrades. As modern CPS designs are complex, it may be infeasible to determine all specifications and mismatches between all subcomponents at design time. Runtime monitoring and verification methods will be developed for inferred specifications to detect mismatches at runtime. When they are identified, a runtime assurance framework building on supervisory control and the Simplex architecture will assure safe CPS runtime operation. The third objective is to evaluate safe CPS upgrades in an example CPS. The results of the other objectives and their ability to ensure safe CPS upgrades will be evaluated in an energy CPS testbed, namely an AC electrical distribution microgrid that interfaces DC-producing renewables like photovoltaics to AC.