Security and privacy concerns in the increasingly interconnected world are receiving much attention from the research community, policymakers, and general public. However, much of the recent and on-going efforts concentrate on security of general-purpose computation and on privacy in communication and social interactions. The advent of cyber-physical systems (e.g., safety-critical IoT), which aim at tight integration between distributed computational intelligence, communication networks, physical world, and human actors, opens new horizons for intelligent systems with advanced capabilities. These systems may reduce number of accidents and increase throughput of transportation networks, improve patient safety, mitigate caregiver errors, enable personalized treatments, and allow older adults to age in their places. At the same time, cyber-physical systems introduce new challenges and concerns about safety, security, and privacy. The proposed project will lead to safer, more secure and privacy preserving CPS. As our lives depend more and more on these systems, specifically in automotive, medical, and Internet-of-Things domains, results obtained in this project will have a direct impact on the society at large. The study of emerging legal and ethical aspects of large-scale CPS deployments will inform future policy decision-making. The educational and outreach aspects of this project will help us build a workforce that is better prepared to address the security and privacy needs of the ever-more connected and technologically oriented society. Cyber-physical systems (CPS) involve tight integration of computational nodes, connected by one or more communication networks, the physical environment of these nodes, and human users of the system, who interact with both the computational part of the system and the physical environment. Attacks on a CPS system may affect all of its components: computational nodes and communication networks are subject to malicious intrusions, and physical environment may be maliciously altered. CPS-specific security challenges arise from two perspectives. On the one hand, conventional information security approaches can be used to prevent intrusions, but attackers can still affect the system via the physical environment. Resource constraints, inherent in many CPS domains, may prevent heavy-duty security approaches from being deployed. This proposal will develop a framework in which the mix of prevention, detection and recovery, and robust techniques work together to improve the security and privacy of CPS. Specific research products will include techniques providing: 1) accountability-based detection and bounded-time recovery from malicious attacks to CPS, complemented by novel preventive techniques based on lightweight cryptography; 2) security-aware control design based on attack resilient state estimator and sensor fusions; 3) privacy of data collected and used by CPS based on differential privacy; and, 4) evidence-based framework for CPS security and privacy assurance, taking into account the operating context of the system and human factors. Case studies will be performed in applications with autonomous features of vehicles, internal and external vehicle networks, medical device interoperability, and smart connected medical home.

The goal of this project is to facilitate timely retrieval of dynamic situational awareness information from field-deployed nodes by an operational center in resource-constrained uncertain environments, such as those encountered in disaster recovery or search and rescue missions. This is an important cyber physical system problem with perspectives drawn at a system and platform level, as well as at the system of systems level. Technology advances allow the deployment of field nodes capable of returning rich content (e.g., video/images) that can significantly aid rescue and recovery. However, development of techniques for acquisition, processing and extraction of the content that is relevant to the operation under resource constraints poses significant interdisciplinary challenges, which this project will address. The focus of the project will be on the fundamental science behind these tasks, facilitated by validation via both in house experimentation, and field tests orchestrated based on input from domain experts. In order to realize the vision of this project, a set of algorithms and protocols will be developed to: (a) intelligently activate field sensors and acquire and process the data to extract semantically relevant information; (b) formulate expressive and effective queries that enable the near-real-time retrieval of relevant situational awareness information while adhering to resource constraints; and, (c) impose a network structure that facilitates cost-effective query propagation and response retrieval. The research brings together multiple sub-disciplines in computing sciences including computer vision, data mining, databases and networking, and understanding the scientific principles behind information management with compromised computation/communication resources. The project will have a significant broader impact in the delivery of effective situational awareness in applications like disaster response. The recent :World Disaster Report" states that there were more than 1 million deaths and $1.5 trillion in damage from disasters within the past decade; the research has the potential to drastically reduce these numbers. Other possible applications are law enforcement and environmental monitoring. The project will facilitate a strong inter-disciplinary education program and provide both undergraduate and graduate students experience with experimentation and prototype development. There will be a strong emphasis on engaging the broader community and partnering with programs that target under-represented students and minorities.

During the last decade, we have witnessed a rapid penetration of autonomous systems technology into aerial, road, underwater, and sea vehicles. The autonomy assumed by these vehicles holds the potential to increase performance significantly, for instance, by reducing delays and increasing capacity, while enhancing safety, in a number of transportation systems. However, to exploit the full potential of these autonomy-enabled transportation systems, we must rethink transportation networks and control algorithms that coordinate autonomous vehicles operating on such networks. This project focuses on the design and operation of autonomy-enabled transportation networks that provide provable guarantees on achieving high performance and maintaining safety at all times. The foundational problems arising in this domain involve taking into account the physics governing the vehicles in order to coordinate them using cyber means. This research effort aims to advance the science of cyber-physical systems by following a unique and radical approach, drawing inspiration and techniques from non-equilibrium statistical mechanics and self-organizing systems, and blending this inspiration with the foundational tools of queueing theory, control theory, and optimization. This approach may allow orders of magnitude improvement in the servicing capabilities of various transportation networks for moving goods or people. The applications include the automation of warehouses, factory floors, sea ports, aircraft carrier decks, transportation networks involving driverless cars, drone-enabled delivery networks, air traffic management, and military logistics networks. The project also aims to start a new wave of classes and tutorials that will create trained engineers and a research community in the area of safe and efficient transportation networks enabled by autonomous cyber-physical systems.

Many practical systems such as smart grid, unmanned aerial vehicles (UAVs) and robotic networks can be categorized as cyber physical systems (CPS). A typical CPS consists of physical dynamics, sensors, communication network and controllers. The communication network is of key importance in CPS, since it mimics the nerve system in the human body. Hence, it is critical to study how the communication network in CPS should be analyzed and designed. Essentially, communications stem from the uncertainty of system under consideration; random perturbations increase the system uncertainty, which is reduced by the control actions in CPS. It is well known that entropy is a measure of system uncertainty. A unified framework of entropy is used for CPS, in which random perturbations create entropy while communications and controls provide negative entropy to compensate the entropy generation. The intellectual merits are the novel framework of entropy for bridging the communications and control in CPS and the new design criterion based on the entropy of system state for CPS. The project's broader significance and importance are the education of various levels of students, the dissemination of results to public, and the impact on everyday life such as the improved agility and robustness of power grids. This project applies the framework of entropy to study the interdependencies of communications and control, thus facilitating the analysis and design of communications in CPS. The following tasks are tackled in the project: (a) Entropy Flow Based Communication Capacity Analysis in which communications in CPS is analyzed by studying the entropy fields in the physical dynamics, thus providing an estimation on the scale (bits/second) of communication capacity budget; (b) Communication Network Topology Design in which the design of the network topology (either physical or logical) is tackled through both optimization-based or heuristic approaches; (c) Online Network Resource Scheduling which refines the network resource scheduling during the operation using both optimization-based and heuristic approaches, within the framework of entropy fields; (d) Hardware Emulation Testbed which delivers a co-simulation testbed based on real time digital power simulator (RTDS) and a communication simulator, in the context of smart grids. Based on the research, new courses are developed. K-12 outreach and various levels of undergraduate/graduate educations are incorporated into the research.

This research addresses the science of Cyber-Physical Systems. In a multi-agent system, each agent is faced with the task of making decisions taking account of the objectives and actions of other agents, as well as the dynamics of the environment. In such a distributed system each agent receives measurements of its environment, and must infer both the state of the world as well as that of the other agents. The intellectual merits of this research are that it develops new efficient techniques for this information processing, which achieve run-time performance using algorithms that have low computational requirements. The project's broader significance and importance are that it will provide new mathematical and computational tools for use in many engineering applications, including the power grid, transportation networks, and other multi-agent systems, and will be transitioned to practice through professional activities such as workshops, development of educational material for graduates, undergraduates and teenagers, and outreach to industry. The underlying mathematical and computation tools for this research are based on new methods for statistical filtering in a dynamic setting. One of the most important techniques for the design of software control systems constructs state estimates which are sufficient statistics for the associated decision problem. However, conventional approaches to sufficient statistics and state estimation do not apply to the multi-agent setting. Recent results have given new sufficient statistics for this setting, and the research develops the theory and algorithms to allow these statistics to be used for multi-agent control of cyber-physical systems.

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.

Additive Manufacturing holds the promise of revolutionizing manufacturing. One important trend is the emergence of cyber additive manufacturing communities for innovative design and fabrication. However, due to variations in materials and processes, design and computational algorithms currently have limited adaptability and scalability across different additive manufacturing systems. This award will establish the scientific foundation and engineering principles needed to achieve adaptability, extensibility, and system scalability in cyber-physical additive manufacturing systems, resulting in high efficiency and accuracy fabrication. The research will facilitate the evolution of existing isolated and loosely-connected additive manufacturing facilities into fully functioning cyber-physical additive manufacturing systems with increased capabilities. The application-based, smart interfacing infrastructure will complement existing cyber additive communities and enhance partnerships between academia, industry, and the general public. The research will contribute to the technology and engineering of Cyber-physical Systems and the economic competitiveness of US manufacturing. This interdisciplinary research will generate new curricular materials and help educate a new generation of cybermanufacturing workforce. The research will establish smart and dynamic system calibration methods and algorithms through deep learning that will enable high-confidence and interoperable cyber-physical additive manufacturing systems. The dynamic calibration and re-calibration algorithms will provide a smart interfacing layer of infrastructure between design models and physical additive manufacturing systems. Specific research tasks include: (1) Establishing smart and fast calibration algorithms to make physical additive manufacturing machines adaptable to design models; (2) Deriving prescriptive compensation algorithms to achieve extensible design models; (3) Dynamic recalibration through deep learning for improved predictive modeling and compensation; and (4) Developing a smart calibration server and APP prototype test bed for scalable additive cyberinfrastructures.

Infrastructure networks are the foundation of the modern world. Their continued reliable and efficient function without exhausting finite natural resources is critical to the security, continued growth and technological advancement of the United States. Currently these systems are in a state of rapid flux due to a collision of trends such as growing populations, expanding integration of information technology, and increasing motivation to adopt sustainable practices. These trends beget both exciting potential benefits and dangerous challenges. Added sensing, communication, and computational capabilities hold the promise of increased reliability, efficiency and sustainability from "smart" infrastructure systems. At the same time, new technologies such as renewable energy resources in power systems, autonomous vehicles, and software defined communication networks, are testing the limits of current operational and market policies. The rapidly changing suite of system components can cause new, unforeseen interactions that can lead to instability, performance deterioration, or catastrophic failures. Achieving the full benefits of these systems will require a shift from the existing focus on approaches that analyze each aspect of interest in isolation, to a more holistic view that encompasses all of the relevant factors such as stability, robustness, performance and efficiency, and takes into account the presence of human participants. This project provides a research roadmap to construct analysis, design and control tools that ensure the seamless integration of computational algorithms, physical components and human interactions in next generation infrastructure systems. Although there has been a great deal of research on stability questions in large scale distributed systems, there has been little effort directed toward questions of performance, robustness and efficiency in these systems, especially those with heterogeneous components and human participants. This research employs coupled oscillator systems as a common modeling framework to (i) characterize stability and performance of infrastructure systems, and (ii) develop distributed controllers that guarantee performance, efficiency and robustness by isolating disturbances and optimizing performance objectives. Practical solutions require that the theory be tightly integrated with the economic mechanisms necessary to incentivize users to enhance system stability, efficiency and reliability; therefore the work will also include the design of economic controls. In order to ground the mathematical foundations, theory and algorithms described above, the results will be applied to three target infrastructure networks where coupled oscillator models have played a foundational role in design and control: power, communication, and transportation systems. This approach allows the development of cross-cutting, fundamental principles that can be applied across problem specific boundaries and ensures that the research makes an impact on these specific infrastructure networks. This project will also incorporate concepts into existing undergraduate and graduate courses.