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

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.

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.

As self-driving cars are introduced into road networks, the overall safety and efficiency of the resulting traffic system must be established and guaranteed. Numerous critical software-related recalls of existing automotive systems indicate that current design practices are not yet up to this challenge. This project seeks to address this problem, by developing methods to analyze and coordinate networks of fully and partially self-driving vehicles that interact with conventional human-driven vehicles on roads. The outcomes of the research are expected to also contribute to the safety of other cyber-physical systems with scalable configurable hierarchical structures, by developing a mathematical framework and corresponding software tools that analyze the safety and reliability of a class of systems that combine physical, mechanical and biological components with purely computational ones. The project research spans four technical areas: autonomous and human-controlled collaborative driving; scheduling computations over heterogeneous distributed computing systems; security and trust in V2X (Vehicle-to-Vehicle and Vehicle-to-Infrastructure) networks; and Verification & Validation of V2X systems through semi-virtual environments and scenarios. The integrating aspect of this research is the development of a distributed system calculus for Cyber-Physical Systems (CPS) that enables modeling, simulation and analysis of collaborative vehicular systems. The development of a comprehensive framework to model, analyze and test reconfiguration, hierarchical control, security and trust differentiates this research from previous attempts to address the same problem. Educational and outreach activities include integration of project research in undergraduate and graduate courses, and a summer camp at Ohio State University for high-school students through the Women in Engineering program.

Traditionally, the design of urban transit services has been based on limited sampling data collected through surveys and censuses, which are often dated and incomplete. Lacking massive online feeds from multiple transit modes makes it hard to achieve real-time equilibrium in demand and supply relationship through cyber-control, which eventually manifests into multiple urban transportation issues: (i) lengthy last-mile transit due to non-supply, (ii) prolonged waiting due to undersupply, and (iii) excessive idle mileage due to oversupply. This project addresses these issues by focusing on two types of transportation systems in metropolitan areas: (i) public bike rental sharing systems and (ii) fleet-oriented ride sharing systems. The public bike rental sharing systems are used to allow commuters to rent bikes near public transit stations for the last mile of their trips. The fleet-oriented ride sharing systems schedule a fleet of participating vehicles for ride sharing among passengers in which shared ridership reduces individual fare paid by passengers, increases the profit of taxi drivers, and can improve the availability of service. The theory and practice of transportation sharing systems have typically focused on isolated individual transportation modes. The project will collect massive multi-modal online feeds from metropolitan information infrastructure to model dynamic behaviors of transportation systems, and then utilize massive micro-level trip information to apply fine-grained real-time control to handle rapid changes in dynamic metropolitan environments. General principles and design methodologies will be designed to build multi-modal, integrated urban transportation systems. These research discoveries will be applied toward commercial applications. Long-term deployment problem of bike stations will be addressed, especially in the low-income districts, to provide suggestions on the station deployment and assessment for specific deployment plans. The project also solves the short-term bike maintenance issue to balance the usage of shared bikes to prevent quick deterioration of rental bikes, and improve availability of bike rental services in real time. This project will also study fleet-oriented ride sharing systems that decide fares based on real-time supply/demand ratio to handle dynamic metropolitan scenarios. This project will support two Ph.D. students who will receive innovation and technology translation training through close interactions with municipal governments and small-business companies. Such partnerships expedite the adoption of cutting-edge technology, evaluate research solutions in operational environments, and obtain user feedback to trigger further innovations. The project will improve the efficiency of existing transportation systems under sharing economy and ultimately the work would noticeably improve the quality of every-day life in metropolitan areas across the United States.

One of the challenges for the future cyber-physical systems is the exploration of large design spaces. Evolutionary algorithms (EAs), which embody a simplified computational model of the mutation and selection mechanisms of natural evolution, are known to be effective for design optimization. However, the traditional formulations are limited to choosing values for a predetermined set of parameters within a given fixed architecture. This project explores techniques, based on the idea of hidden genes, which enable EAs to select a variable number of components, thereby expanding the explored design space to include selection of a system's architecture. Hidden genetic optimization algorithms have a broad range of potential applications in cyber-physical systems, including automated construction systems, transportation systems, micro-grid systems, and space systems. The project integrates education with research by involving students ranging from high school through graduate school in activities commensurate with their skills, and promotes dissemination of the research results through open source distribution of algorithm implementation code and participation in the worldwide Global Trajectory Optimization Competition. Instead of using a single layer of coding to represent the variables of the system in current EAs, this project investigates adding a second layer of coding to enable hiding some of the variables, as needed, during the search for the optimal system's architecture. This genetic hiding concept is found in nature and provides a natural way of handling system architectures covering a range of different sizes in the design space. In addition, the standard mutation and selection operations in EAs will be replaced by new operations that are intended to extract the full potential of the hidden gene model. Specific applications include space mission design, microgrid optimization, and traffic network signal coordinated planning.

Securing critical networked cyber-physical systems (NCPSs) such as the power grid or transportation systems has emerged as a major national and global priority. The networked nature of such systems renders them vulnerable to a range of attacks both in cyber and physical domains as corroborated by recent threats such as the Stuxnet worm. Developing security mechanisms for such NCPSs significantly differs from traditional networked systems due to interdependence between cyber and physical subsystems (with attacks originating from either subsystem), possible cooperation between attackers or defenders, and the presence of human decision makers in the loop. The main goal of this research is to develop the necessary science and engineering tools for designing NCPS security solutions that are applicable to a broad range of application domains. This project will develop a multidisciplinary framework that weaves together principles from cybersecurity, control theory, networking and criminology. The framework will include novel security mechanisms for NCPSs founded on solid control-theoretic and related notions, analytical tools that allow incorporation of bounded human rationality in NCPS security, and experiments with real-world attack scenarios. A newly built cross-institutional NCPS simulator will be used to evaluate the proposed mechanisms in realistic environments. This research transcends specific cyber-physical systems domains and provides the necessary tools to building secure and trustworthy NCPSs. The broader impacts include a new infrastructure for NCPS research and education, training of students, new courses, and outreach events focused on under-represented student groups