This project focuses on modeling and mitigating cyber attacks on Cyber-Physical Systems (CPS), which are increasingly prevalent in all aspects of society such as health care, energy, and transportation. Attacks initiated on the cyber components of CPS can be mounted remotely at little economic cost and can significantly degrade the safety and performance of CPS due to the tight coupling between cyber and physical components. This project develops a passivity-based framework for modeling, composing, and mitigating multiple attacks on CPS. Passivity is an energy dissipation property that provides basic rules for analyzing and composing interconnected systems. In addition to passive adversary models and composition rules, this project will investigate techniques for decomposition of composed attack models into basic primitives which will lead to development of new mitigation strategies. Approximate bi-simulation techniques will be introduced to verify the developed adversary models and mitigation strategies. The proposed approach is general and will be applicable to mitigate CPS security challenges arising in multiple sectors including transportation, energy, manufacturing, and others. The goals of the project are as follows: (a) research and development of passive dynamical models of multiple attacks, as well as characterization of the class of attacks that admit a passive representation; (b) investigation and development of passivity-based composition and decomposition rules, enabling identification of new attack variants and associated mitigation strategies; (c) research and development of approximate techniques for verification of composed adversary models and mitigation strategies; and (d) validation and prototyping of the proposed models through an experimental testbed.

The electric power grid is a complex cyber-physical system (CPS) that forms the lifeline of modern society. Cybersecurity and resiliency of the power grid is of paramount importance to national security and economic well-being. CPS security testbeds are enabling technologies that provide realistic experimental platforms for the evaluation and validation of security technologies within controlled environments, and they also enable the exploration of robust security solutions. The project has two objectives: (a) to develop innovative architectures, abstractions, models, and algorithms for large-scale CPS security testbeds; and (b) to design and implement a high-fidelity, scalable, open-access CPS security testbed for the smart grid, and to conduct research experimentation. The testbed integrates appropriate cyber-control-physical hardware/software components, models, and algorithms in a modular design that enables federation of smaller testbeds to form a large-scale virtual experimental environment. The use cases for the testbed include vulnerability assessment, risk assessment, risk mitigation studies, and attack-defense exercises. The project also aims to develop standardized datasets, models, libraries, and use cases, and make the testbed available to a broader research community through an open-, remote-access model by leveraging collaboration from academic and industry partners. Besides contributing to research and technology that will enable a future electric power grid that is secure and resilient, this project develops and disseminates innovative curriculum modules including CPS Cyber Defense Competitions (CPS-CDC) for imparting security knowledge to students via an inquiry-based learning paradigm. The project also mentors students, including underrepresented minorities, in thesis work and Capstone projects, and exposes high-school students to cybersecurity concepts via testbed demonstrations.

The confluence of new networked sensing technologies (e.g., cameras), distributed computational resources (e.g., cloud computing), and algorithmic advances (e.g., computer vision) are offering new and exciting opportunities for solving a variety of new problems that are of societal importance including emergency response, disaster recovery, surveillance, and transportation. Solutions to this new class of problems, referred to as "situation awareness" applications, include surveillance via large-scale distributed camera networks and personalized traffic alerts in vehicular networks using road and traffic sensing. A breakthrough in system software technology is needed to meet the challenges posed by these applications since they are latency-sensitive, data intensive, involve heavy-duty processing, and must run 24x7 while dealing with the vagaries of the physical world. This project aims to make such a breakthrough, through new distributed programming idioms and resource allocation strategies. To better identify the challenges posed by situation awareness applications, the project includes experimental deployment of the new technologies in partnership with the City of Baton Rouge, Louisiana. The central activity is to develop appropriate system abstractions for design of situation awareness applications and encapsulate them in distributed programming idioms for domain experts (e.g., vision researchers). The resulting programming framework allows association of critical attributes such as location, time, and mobility with sensed data to reason about causal events along these axes. To meet the latency constraints of these applications, the project develops geospatial resource allocation mechanisms that complement and support the distributed programming idioms, extending the utility-computing model of cloud computing to the edge of the network. Since the applications often have to work with inexact knowledge of what is happening in the physical environment, owing to limitations of the distributed sensing sources, the project also investigates system support for application-specific information fusion and spatio-temporal analyses to increase the quality of results. Efforts toward development of a future cyber-physical systems workforce include creation of a new multidisciplinary curriculum around situation awareness, exploration of new immersive learning pedagogical styles, and mentoring and providing research experience to undergraduate students through research experiences and internships aimed at increasing participation of women and minorities.

Tracking Fish Movement with a School of Gliding Robotic Fish This project is focused on developing the technology for continuously tracking the movement of live fish implanted with acoustic tags, using a network of relatively inexpensive underwater robots called gliding robotic fish. The research addresses two fundamental challenges in the system design: (1) accommodating significant uncertainties due to environmental disturbances, communication delays, and apparent randomness in fish movement, and (2) balancing competing objectives (for example, accurate tracking versus long lifetime for the robotic network) while meeting multiple constraints on onboard computing, communication, and power resources. Fish movement data provide insight into choice of habitats, migratory routes, and spawning behavior. By advancing the state of the art in fish tracking technology, this project enables better-informed decisions for fishery management and conservation, including control of invasive species, restoration of native species, and stock assessment for high-valued species, and ultimately contributes to the sustainability of fisheries and aquatic ecosystems. By advancing the coordination and control of gliding robotic fish networks and enabling their operation in challenging environments such as the Great Lakes, the project also facilitates the practical adoption of these robotic systems for a myriad of other applications in environmental monitoring, port surveillance, and underwater structure inspection. The project enhances several graduate courses at Michigan State University, and provides unique interdisciplinary training opportunities for students including those from underrepresented groups. Outreach activities, including robotic fish demos, museum exhibits, teacher training, and "Follow That Fish" smartphone App, are specifically designed to pique the interest of pre-college students in science and engineering. The goal of this project is to create an integrative framework for the design of coupled robotic and biological systems that accommodates system uncertainties and competing objectives in a rigorous and holistic manner. This goal is realized through the pursuit of five tightly coupled research objectives associated with the application of tracking and modeling fish movement: (1) developing new robotic platforms to enable underwater communication and acoustic tag detection, (2) developing robust algorithms with analytical performance assurance to localize tagged fish based on time-of-arrival differences among multiple robots, (3) designing hidden Markov models and online model adaptation algorithms to capture fish movement effectively and efficiently, (4) exploring a two-tier decision architecture for the robots to accomplish fish tracking, which incorporates model-predictions of fish movement, energy consumption, and mobility constraints, and (5) experimentally evaluating the design framework, first in an inland lake for localizing or tracking stationary and moving tags, and then in Thunder Bay, Lake Huron, for tracking and modeling the movement of lake trout during spawning. This project offers fundamental insight into the design of robust robotic-physical-biological systems that addresses the challenges of system uncertainties and competing objectives. First, a feedback paradigm is presented for tight interactions between the robotic and biological components, to facilitate the refinement of biological knowledge and robotic strategies in the presence of uncertainties. Second, tools from estimation and control theory (e.g., Cramer-Rao bounds) are exploited in novel ways to analyze the performance limits of fish tracking algorithms, and to guide the design of optimal or near-optimal tradeoffs to meet multiple competing objectives while accommodating onboard resource constraints. On the biology side, continuous, dynamic tracking of tagged fish with robotic networks represents a significant step forward in acoustic telemetry, and results in novel datasets and models for advancing fish movement ecology.

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.

This Cyber-Physical Systems (CPS) award supports research to enable the automated monitoring of building and infrastructure construction projects. The purpose of construction monitoring is to provide developers, contractors, subcontractors, and tradesmen with the information they need to easily and quickly make project control decisions. These decisions have a direct impact on the overall efficiency of a construction project. Given that construction is a $800 billion industry, gains in efficiency could lead to enormous cost savings, benefiting both the U.S. economy and society. In particular, both construction cost and delivery time could be significantly reduced by automated tools to assess progress towards completion (progress monitoring) and how construction resources are being utilized (activity monitoring). These tools will be provided by advances in the disciplines of computer vision, robotics, and construction management. The interdisciplinary nature of this project will create synergy among these disciplines and will positively influence engineering education. Partnerships with industry will also ensure that these advances have a positive impact on construction practice. The process of construction monitoring involves data collection, analysis, and reporting. Research will address the existing scientific challenges to automating these three activities. Data collection will be automated by recording video with aerial robots and a network of cameras. Key research objectives are to derive planning algorithms that guarantee complete coverage of a construction site and to derive vision-based control algorithms that enable robust placement and retrieval of cameras. Analysis will be automated with a digital building information model with respect to which construction resources can be tracked. Key research objectives are to improve the efficiency and reliability of image-based reconstruction, to recognize material properties as well as geometry, to establish a formal language for representing construction activities, and to extend a parts-based approach for automated activity recognition. Reporting will be automated with a ubiquitous display of the digital building information model. Key research objectives are to formalize a constraint construction ontology with associated classification mechanisms and allow for systematic earned value analysis of construction progress. Experimental validation will focus on monitoring construction of substructure and superstructure skeletal elements in buildings and infrastructure systems as well as the associated earth-moving, concrete placement, and steel erection activities that are common in construction projects.

This project aims to enable cyber-physical systems that can be worn on the body in order to one day allow their users to touch, feel, and manipulate computationally simulated three-dimensional objects or digital data in physically realistic ways, using the whole hand. It will do this by precisely measuring touch and movement-induced displacements of the skin in the hand, and by reproducing these signals interactively, via new technologies to be developed in the project. The resulting systems will offer the potential to impact a wide range of human activities that depend on touch and interaction with the hands. The project seeks to enable new applications for wearable cyber physical interfaces that may have broad applications in health care, manufacturing, consumer electronics, and entertainment. Although human interactive technologies have advanced greatly, current systems employ only a fraction of the sensorimotor capabilities of their users, greatly limiting applications and usability. The development of whole-hand haptic interfaces that allow their wearers to feel and manipulate digital content has been a longstanding goal of engineering research, but has remained far from reality. The reason can be traced to the difficulty of reproducing or even characterizing the complex, action-dependent stimuli that give rise to touch sensations during everyday activities. This project will pioneer new methods for imaging complex haptic stimuli, consisting of movement dependent skin strain and contact-induced surface waves propagating in skin, and for modeling the dependence of these signals on hand kinematics during grasping. It will use the resulting fundamental advances to catalyze the development of novel wearable CPS, in the form of whole-hand haptic interfaces. The latter will employ surface wave and skin strain feedback to supply haptic feedback to the hand during interaction with real and computational objects, enabling a range of new applications in VR. The project will be executed through research in three main research areas. In the first, it will utilize novel contact and non-contact techniques based on data acquired through on-body sensor arrays to measure whole-hand mechanical stimuli and grasping kinematics at high spatial and temporal resolution. In a second research area, it will undertake data-driven systems modeling and analysis of statistical contingencies between the kinematic and cutaneous sensed during everyday activities. In a third research area, it will engineer and perceptually evaluate novel cyber physical systems consisting of haptic interfaces for whole hand interaction. In order to further advance the applications of these systems in medicine, through a collaboration with the Drexel College of Medicine, the project will develop new methods for assessing clinical skills of palpation during medical examination, with the aim of improving the efficacy of what is often the first, most common, and best opportunity for diagnosis, using physician's own sense of touch.

This project addresses the foundational problem of knowledge within cyber-physical systems (CPS), i.e., systems that combine aspects such as communication, computation, and physics. A single system observes its environment through sensors and interacts through actuators. Neither is perfect. Thus, the CPS's internal view of the world is blurry and its actions are imprecise. CPS are still analyzed with methods that do not distinguish between truth in the world and an internal view thereof, resulting in a mismatch between the behavior of theoretical models and their real-world counterparts. How could they be trusted to perform safety-critical tasks? This project addresses this critical insufficiency by developing methods to reason about knowledge and learning in CPS. The project pursues the development of new logical principles for verifying knowledge-aware CPS. This project investigates how to make the mismatch between the world and the partial perception through sensors explicit and how to achieve provably correct control in theory as well as practice despite this mismatch. By investigating changing knowledge in a changing world, this project contributes to a fundamental feature without which CPS can never be truly safe and efficient at the same time. The project's broader significance and importance are a result of the widespread attention that CPS gain in many safety-critical areas, such as in aviation and automotive industries. One reason for safety gaps in such CPS is that formal verification techniques are still largely knowledge-agnostic, and verifiable solutions overly pessimistic. This project addresses these issues and provides tools that allow for incorporating knowledge about the environment's intentions into the models to derive provably correct, but justifiably optimistic, and thus efficient, behavior. By their logical nature, these techniques are applicable to a wide range of CPS and, thus, contribute significantly to numerous applications. Results obtained within this project will be demonstrated in CPS models and laboratory robot scenarios, and will be shared in courses and with industrial partners. The technical approach that this project pursues develops a new modeling language, logic, and proof calculus for verifying knowledge-aware CPS. The knowledge paradigm used allows CPS controllers to seamlessly acquire knowledge about the world but also about other agents in the system, i.e., other controllers. Knowledge is the key to interactions between different agents. This project investigates how an explicit model of world perception and agent intentions - and knowledge of these perceptions and intentions - allows CPS agents to act, based on more efficient, but still provably safe control in multi-agent scenarios. The methods will be implemented in the verification tool KeYmaera and demonstrated in formal verification on different case study applications such as car scenarios.

In the next few decades, autonomous vehicles will become an integral part of the traffic flow on highways. However, they will constitute only a small fraction of all vehicles on the road. This research develops technologies to employ autonomous vehicles already in the stream to improve traffic flow of human-controlled vehicles. The goal is to mitigate undesirable jamming, traffic waves, and to ultimately reduce the fuel consumption. Contemporary control of traffic flow, such as ramp metering and variable speed limits, is largely limited to local and highly aggregate approaches. This research represents a step towards global control of traffic using a few autonomous vehicles, and it provides the mathematical, computational, and engineering structure to address and employ these new connections. Even if autonomous vehicles can provide only a small percentage reduction in fuel consumption, this will have a tremendous economic and environmental impact due to the heavy dependence of the transportation system on non-renewable fuels. The project is highly collaborative and interdisciplinary, involving personnel from different disciplines in engineering and mathematics. It includes the training of PhD students and a postdoctoral researcher, and outreach activities to disseminate traffic research to the broader public. This project develops new models, computational methods, software tools, and engineering solutions to employ autonomous vehicles to detect and mitigate traffic events that adversely affect fuel consumption and congestion. The approach is to combine the data measured by autonomous vehicles in the traffic flow, as well as other traffic data, with appropriate macroscopic traffic models to detect and predict congestion trends and events. Based on this information, the loop is closed by carefully following prescribed velocity controllers that are demonstrated to reduce congestion. These controllers require detection and response times that are beyond the limit of a human's ability. The choice of the best control strategy is determined via optimization approaches applied to the multiscale traffic model and suitable fuel consumption estimation. The communication between the autonomous vehicles, combined with the computational and control tasks on each individual vehicle, require a cyber-physical approach to the problem. This research considers new types of traffic models (micro-macro models, network approaches for higher-order models), new control algorithms for traffic flow regulation, and new sensing and control paradigms that are enabled by a small number of controllable systems available in a flow.

In the next few decades, autonomous vehicles will become an integral part of the traffic flow on highways. However, they will constitute only a small fraction of all vehicles on the road. This research develops technologies to employ autonomous vehicles already in the stream to improve traffic flow of human-controlled vehicles. The goal is to mitigate undesirable jamming, traffic waves, and to ultimately reduce the fuel consumption. Contemporary control of traffic flow, such as ramp metering and variable speed limits, is largely limited to local and highly aggregate approaches. This research represents a step towards global control of traffic using a few autonomous vehicles, and it provides the mathematical, computational, and engineering structure to address and employ these new connections. Even if autonomous vehicles can provide only a small percentage reduction in fuel consumption, this will have a tremendous economic and environmental impact due to the heavy dependence of the transportation system on non-renewable fuels. The project is highly collaborative and interdisciplinary, involving personnel from different disciplines in engineering and mathematics. It includes the training of PhD students and a postdoctoral researcher, and outreach activities to disseminate traffic research to the broader public. This project develops new models, computational methods, software tools, and engineering solutions to employ autonomous vehicles to detect and mitigate traffic events that adversely affect fuel consumption and congestion. The approach is to combine the data measured by autonomous vehicles in the traffic flow, as well as other traffic data, with appropriate macroscopic traffic models to detect and predict congestion trends and events. Based on this information, the loop is closed by carefully following prescribed velocity controllers that are demonstrated to reduce congestion. These controllers require detection and response times that are beyond the limit of a human's ability. The choice of the best control strategy is determined via optimization approaches applied to the multiscale traffic model and suitable fuel consumption estimation. The communication between the autonomous vehicles, combined with the computational and control tasks on each individual vehicle, require a cyber-physical approach to the problem. This research considers new types of traffic models (micro-macro models, network approaches for higher-order models), new control algorithms for traffic flow regulation, and new sensing and control paradigms that are enabled by a small number of controllable systems available in a flow.