Cyber-physical systems (CPSs) allow computer systems to monitor and control the physical world in a new way that could revolutionize many areas of science and engineering. However, they are often too complex for non-specialists to use. The aim of this work is to develop new technology to manage this complexity, enabling scientists and engineers to use CPSs just like other tools and instruments. This research takes a comprehensive approach to macroprogramming -- the task of programming an entire network of devices as a single, programmable substrate. This research exploits global, network-wide information about a CPS provided by a macroprogram to improve traditional software engineering techniques such as testing, debugging, analysis, and optimization. New techniques are being developed that use global information to optimize system performance, automatically generate test cases, and reduce the state space for analysis. This work is developing new programming abstractions that allow the separation of the application logic from quality-of-service requirements and hardware requirements, improving code portability and reuse. This research will produce a comprehensive development environment for CPSs called MacroLab. The new tools developed will greatly simplify the process of their programming and make them more accessible to non-experts. By taking a holistic view of the network and its software, MacroLab will manage a range of complex, interacting issues that would be extremely difficult to manage by hand. MacroLab will be tested pilot studies, including environmental monitoring. A graduate CPS course will be developed. MacroLab will be used for course experiments and in senior capstone projects.

Many critical infrastructures, such as the power grid, are complex cyber physical systems (CPS). Protecting these systems against cyber-attacks is of paramount importance to national security and economic well-being. Risk assessment considering cyber-attacks against critical infrastructures is not well understood due to ever growing, dynamic threat landscape coupled with complex cyber-physical interactions in these systems. In addition, there is a compelling need to create environments in which realistic attack-defense experiments (including risk assessment and risk mitigation) and training exercises can be safely conducted to advance the science and workforce development in this important area of national need. This project has two key goals: (1) the short-term goal is to design, develop, and demonstrate a cyber defense exercise for improving the security of CPS systems in alignment with the NIST/US Ignite Global Cities Team Challenge; and (2) the long-term goal is to explore fundamental models and algorithms for cyber risk assessment and mitigation. The project makes synergistic federation of three existing security testbeds hosted at Iowa State University and the University of Southern California to create a realistic environment for conducting CPS security experimentation and security preparedness and training exercise, like the North American Reliability Corporation (NERC) GridEx. The intellectual merit of the project lies in two key contributions: (i) realistic experimentations on CPS security testbed federation, and (ii) the development of a novel methodology for cyber risk modeling of CPS systems. The broader impacts of the project lie in developing realistic attack-defense scenarios and learning/training modules that enable academic researchers, students, and industry practitioners to systematically understand, analyze, and improve the security and resiliency of critical infrastructures.

Device authentication and identification has been recently cited as one of the most pressing security challenges facing the Internet of things (IoT). In particular, the open-access nature of the IoT renders it highly susceptible to insider attacks. In such attacks, adversaries can capture or forge the identity of the small, resource constrained IoT devices and, thus, bypass conventional authentication methods. Such attacks are challenging to defend against due to the apparent legitimacy of the adversaries' devices. The primary goal of this research is to overcome this challenge by developing new authentication methods that supplement traditional security solutions with cyber-physical fingerprints extracted from the IoT devices' environment. This project will develop a novel machine learning framework that enables the IoT to dynamically identify, classify, and authenticate devices based on their cyber-physical environment and with limited available prior data. This will result in the creation of environment-based IoT device credentials that can serve as a means of attestation, not only on the legitimacy of a device's identity, but also on the validity of the physical environment it claims to monitor and the actions it claims to be performing over time. The framework will also encompass an experimental IoT software platform that will be built to validate the proposed research. Owing to a partnership with the NIST Global City Teams Challenge (GCTC) project "Bringing Internet of Things Know-How to High School Students", a collaboration with IoT-DC, Arlington County, VA, and other entities, the proposed research will train high school students, STEM educators, and a broad community on a variety of research topics that will include IoT security, cyber-physical systems, and data analytics. The broader impacts will also include the creation of an interdisciplinary workforce focused on securing tomorrow's smart cities.

This Data-driven Decision-making in Cyber-physical systems (CPS) project focuses on bringing tools from data science and systems science together to develop new tools for analyzing and making accurate decisions in complex cyber-physical systems (e.g., power-grid, transportation network, power plants and smart buildings) to make them safer, more efficient and highly secure. This project develops algorithms, implements software and demonstrates proof-of-concept using large integrated building system as a challenge application area. Potential advantages of the tools developed in this research over current methods will be higher degree of accuracy, increased automation and lower cost of implementation. Majority of state-of-the-art methods use ad-hoc rules and physics-based models for such problems. However, they lack in accuracy and scalability due to the very complex nature of current and future large interconnected systems. The tools developed in this project will alleviate these issues significantly via intelligent use of large volume of data generated from the systems. The theoretical aspect of the research will make use of inherently multidisciplinary concepts from Nonlinear Dynamics, Information Theory, Machine Learning and Statistical Mechanics. The research project primarily supports interdisciplinary education and career development of graduate students as well as offers education and outreach programs to high school and undergraduate students in STEM disciplines. The project engages the Center for Building Energy Research (CBER) at Iowa State to demonstrate success on a real platform. The center provides a unique opportunity to the researchers to test and validate the tools on the Interlock House test bed which is a high end field laboratory for energy efficiency research and data validation. This enhances the potential of transitioning the new technology toward commercial reality.Soumik

This cross-disciplinary research proposes a patient-specific cost-saving approach to the design and optimization of healthcare cyber-physical systems (HCPS). The HCPS computes the patient's physiological state based on sensors, communicates this information via a network from home to hospital for quantifying risk indices, signals the need for critical medical intervention in real time, and controls vital health signals (e.g., cardiac rhythm, blood glucose). The research proposed under the HCPS paradigm will treat the human body as a complex system. It will entail the development of mathematical models that capture the time-dependence and fractal behavior of physiological processes and the design of quality-of-life (QoL) control strategies for medical devices. The research will advance the understanding of the correlations between physiological processes, drug treatment, stress level and lifestyle. To date, the complex interdependence, variability and individual characteristics of physiological processes have not been taken into account in the design of medical devices and artificial organs. The existing mathematical approaches rely on reductionist and Markovian assumptions. This research project will rethink the theoretical foundations for the design of healthcare cyber-physical systems by capturing the interdependencies and fractal characteristics of physiological processes within a highly dynamic network. To establish the theoretical foundations of HCPS, a three-step approach will be followed: (i) construct a multi-scale non-equilibrium statistical physics inspired framework for patient modeling that captures the time dependence, non-Gaussian behavior, interdependencies and multi-fractal behavior of physiological processes; (ii) develop adaptive patient-specific and physiology-aware (multi-fractal) close-loop control algorithms for dynamic complex networks; (iii) design algorithms and methodologies for the HCPS networked components that account for biological and technological constraints. This research will significantly contribute to early chronic disease detection and treatment. Models and implementable algorithms, which can both predict physiological dynamics and assess the risk of acute and chronic diseases, will be valuable instruments for patient-centered healthcare. This in-depth mathematical analysis of physiological complexity facilitates a transformative multimodal and multi-scale approach to CPS design with healthcare applications. The project not only addresses the current scientific and technological gap in CPS, but can also foster new research directions in related fields such as the study of interdependent networks with implications for understanding homeostasis and diseases and the study and control of complex systems. The cyber-physical systems designed under this newly proposed paradigm will have vital social and economic implications, including the improvement of QoL and the reduction of lost productivity rates due to chronic diseases. The project will offer interdisciplinary training for graduate, undergraduate and K-12 students. The PI will integrate the research results within his courses at University of Southern California and make them widely available through the project website. Moreover, the PI will enhance civic engagement by involving college and K-12 students in community outreach activities that will raise awareness of the important role of health monitoring.

Every year around 30,000 fatalities and 2.2 million injuries happen on US roads. The problem is compounded with huge economic losses due to traffic congestions. Advances in Cooperative Vehicle Efficiency and Safety (CVES) systems promise to significantly reduce the human and economic cost of transportation. However, large scale deployment of such systems is impeded by significant technical and scientific gaps, especially when it comes to achieving real-time and high accuracy situational awareness for cooperating vehicles. This CAREER project aims at closing these gaps through developing fundamental information networking methodologies for coordinated control of automated systems. These methodologies will be based on the innovative concept of modeled knowledge propagation. In addition, the educational component of this project integrates interdisciplinary Cyber-Physical Systems (CPS) subjects on the design of automated networked systems into graduate and undergraduate training modules. For robust operation, CVES systems require each vehicle to have reliable real-time awareness of the state of other coordinated vehicles. This project addresses the critical need for robust control-oriented situational awareness by developing a multi-resolution information networking methodology that is model- and context-aware. The approach is to develop the novel concepts of model communication and its derived multi-resolution networking. Context-aware model-communication relies on transmission and synchronization of models (e.g., stochastic hybrid system structures and parameters) instead of raw measurements. This allows for high fidelity synchronization of dynamical models of CVES over networks. Multi-resolution networking concept is enabled through scalable representations of models. Multi resolution models allow in-network adaptation of model fidelity to available network resources. The result is robustness of CVES to network service variability. The successful deployment of CVES, even partially, will provide significant societal benefits through reduced traffic accidents and improved efficiency. This project will enable large scale CVES deployment by addressing its scalability challenge. In addition, methodologies developed in this project will be crucial to emerging autonomous vehicles, which are also expected to coordinate their actions over communication networks. The fundamental research outcomes on knowledge propagation through network synchronization of dynamical models will be broadly applicable in other CPS domains such as smart grid. The educational component of this project will target training of CPS researchers and engineers on subjects in intelligent transportation and energy systems.

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.

Brain-computer interfaces (BCIs) are cyber-physical systems (CPSs) that record human brain waves and translate them into the control commands for external devices such as computers and robots. They may allow individuals with spinal cord injury (SCI) to assume direct brain control of a lower extremity prosthesis to regain the ability to walk. Since the lower extremity paralysis due to SCI leads to as much as $50 billion of health care cost each year in the US alone, the use of a BCI-controlled lower extremity prosthesis to restore walking can have a significant public health impact. Recent results have demonstrated that a person with paraplegia due to SCI can use a non-invasive BCI to regain basic walking. While encouraging, this BCI is unlikely to become a widely adopted solution since the poor signal quality of non-invasively recorded brain waves may lead to unreliable BCI operation. Moreover, lengthy and tedious mounting procedures of the non-invasive BCI systems are impractical. A permanently implantable BCI CPS can address these issues, but critical challenges must be overcome to achieve this goal, including the elimination of protruding electronics and reliance on an external computer for brain signal processing. The goal of this study is to develop a benchtop version of a fully implantable BCI CPS, capable of acquiring electrocorticogram signals, recorded directly from the surface of the brain, and analyzing them internally to enable direct brain control of a robotic gait exoskeleton (RGE) for walking. The BCI CPS will be designed as a low-power system with revolutionary adaptive power management in order to meet stringent heat and power consumption constraints for future human implantation. Comprehensive measurements and benchtop tests will ensure proper function of BCI CPS. Finally, the system will be integrated with an RGE, and its ability to facilitate brain-controlled walking will be tested in a small group of human subjects. The successful completion of this project will have broad bioengineering and scientific impact. It will revolutionize medical device technology by minimizing power consumption and heat production while enabling complex operations to be performed. The study will also help deepen the understanding of how the human brain controls walking, which has long been a mystery to neuroscientists. Finally, this study?s broader impact is to promote education and lifelong learning in engineering students and the community, broaden the participation of underrepresented groups in engineering, and increase the scientific literacy of persons with disabilities. Research opportunities will be provided to (under-)graduate students. Their findings will be broadly disseminated and integrated into teaching activities. To inspire underrepresented K-12 and community college students to pursue higher education in STEM fields, and to increase the scientific literacy of persons with disabilities, outreach activities will be undertaken in the form of live scientific exhibits and actual BCI demonstrations. Recent results have demonstrated that a person with paraplegia due to SCI can use an electroencephalogram (EEG)-based BCI to regain basic walking. While encouraging, this EEG-based BCI is unlikely to become a widely adopted solution due to EEG?s inherent noise and susceptibility to artifacts, which may lead to unreliable operation. Also, lengthy and tedious EEG (un-)mounting procedures are impractical. A permanently implantable BCI CPS can address these issues, but critical CPS challenges must be overcome to achieve this goal, including the elimination of protruding electronics and reliance on an external computer for neural signal processing. The goal of this study is to implement a benchtop analogue of a fully implantable BCI CPS, capable of acquiring high-density (HD) electrocorticogram (ECoG) signals, and analyzing them internally to facilitate direct brain control of a robotic gait exoskeleton (RGE) for walking. The BCI CPS will be designed as a low-power modular system with revolutionary adaptive power management in order to meet stringent heat dissipation and power consumption constraints for future human implantation. The first module will be used for acquisition of HD-ECoG signals. The second module will internally execute optimized BCI algorithms and wirelessly transmit commands to an RGE for walking. System and circuit-level characterizations will be conducted through comprehensive measurements. Benchtop tests will ensure the proper system function and conformity to biomedical constraints. Finally, the system will be integrated with an RGE, and its ability to facilitate brain-controlled walking will be tested in a group of human subjects.The successful completion of this project will have broad bioengineering and scientific impact. It will revolutionize medical device technology by minimizing power consumption and heat dissipation while enabling complex algorithms to be executed in real time. The study will also help deepen the physiological understanding of how the human brain controls walking. This study will promote education and lifelong learning in engineering students and the community, broaden the participation of underrepresented groups in engineering, and increase the scientific literacy of persons with disabilities. Research opportunities will be provided to under-graduate students. Their findings will be broadly disseminated and integrated into teaching activities. To inspire underrepresented K-12 and community college students to pursue higher education in STEM fields, and to increase the scientific literacy of persons with disabilities, outreach activities will be undertaken in the form of live scientific exhibits and actual BCI demonstrations.

Designing semi-autonomous networks of miniature robots for inspection of bridges and other large civil infrastructure According to the U.S. Department of Transportation, the United States has 605102 bridges of which 64% are 30 years or older and 11% are structurally deficient. Visual inspection is a standard procedure to identify structural flaws and possibly predict the imminent collapse of a bridge and determine effective precautionary measures and repairs. Experts who carry out this difficult task must travel to the location of the bridge and spend many hours assessing the integrity of the structure. The proposal is to establish (i) new design and performance analysis principles and (ii) technologies for creating a self-organizing network of small robots to aid visual inspection of bridges and other large civilian infrastructure. The main idea is to use such a network to aid the experts in remotely and routinely inspecting complex structures, such as the typical girder assemblage that supports the decks of a suspension bridge. The robots will use wireless information exchange to autonomously coordinate and cooperate in the inspection of pre-specified portions of a bridge. At the end of the task, or whenever possible, they will report images as well as other key measurements back to the experts for further evaluation. Common systems to aid visual inspection rely either on stationary cameras with restricted field of view, or tethered ground vehicles. Unmanned aerial vehicles cannot access constricted spaces and must be tethered due to power requirements and the need for uninterrupted communication to support the continual safety critical supervision by one or more operators. In contrast, the system proposed here would be able to access tight spaces, operate under any weather, and execute tasks autonomously over long periods of time. The fact that the proposed framework allows remote expert supervision will reduce cost and time between inspections. The added flexibility as well as the increased regularity and longevity of the deployments will improve the detection and diagnosis of problems, which will increase safety and support effective preventive maintenance. This project will be carried out by a multidisciplinary team specialized in diverse areas of cyber-physical systems and robotics, such as locomotion, network science, modeling, control systems, hardware sensor design and optimization. It involves collaboration between faculty from the University of Maryland (UMD) and Resensys, which specializes in remote bridge monitoring. The proposed system will be tested in collaboration with the Maryland State Highway Administration, which will also provide feedback and expertise throughout the project. This project includes concrete plans to involve undergraduate students throughout its duration. The investigators, who have an established record of STEM outreach and education, will also leverage on exiting programs and resources at the Maryland Robotics Center to support this initiative and carry out outreach activities. In order to make student participation more productive and educational, the structure of the proposed system conforms to a hardware architecture adopted at UMD and many other schools for the teaching of undergraduate courses relevant to cyber-physical systems and robotics. This grant will support research on fundamental principles and design of robotic and cyber-physical systems. It will focus on algorithm design for control and coordination, network science, performance evaluation, microfabrication and system integration to address the following challenges: (i) Devise new locomotion and adhesion principles to support mobility within steel and concrete girder structures. (ii) Investigate the design of location estimators, omniscience and coordination algorithms that are provably optimal, subject to power and computational constraints. (iii) Methods to design and analyze the performance of energy-efficient communication protocols to support robot coordination and localization in the presence of the severe propagation barriers caused by metal and concrete structures of a bridge.