The focus of this project is on creating new techniques for understanding population analytics over a space of interest, e.g., a shopping mall, a busy street, or an entire city. Knowledge of population behavior important for many applications. For instance, knowledge of which are the busy corners of city sidewalk can provide city planners with input on where to invest city resources. Knowledge of where people congregate in a shopping mall allows officials to plan where to provide useful services, e.g., information kiosks, floor plans, and more. The process of gathering population analytics today is tedious -- some stores and shops use manual people counters to track how many persons are entering wireless technologies. The technical contributions of this project are two-fold. First, it is attempting to reduce the complexity of determining location of people by reducing the number of infrastructure points needed. Second, automated approaches to population analytics are fraught with privacy concerns, and this project is examining techniques that mitigate such concerns. Personnel involved in this project will be trained in significant technical skills across a broad set of domains including wireless technologies, privacy techniques, and machine learning. To demonstrate the feasibility of this project, the PI team is deploying a version of the system in an urban downtown area of Madison, WI. The team is collaborating with a number of local partners -- the city of Madison, the University of Wisconsin Bookstore, 5NINES (a local Internet Service Provider), and a few local participants. Together they are entering this technology demonstration as part of the Global City Teams Challenge being hosted by NSF and NIST.

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.

Human-in-the-loop control strategies in which the user performs a task better, and feels more confident to do so, is an important area of research for cyber-physical systems. Humans are very adept at learning to control complex systems, particularly those with non-intuitive kinematic constraints (e.g., cars, bicycles, wheelchairs, steerable needles). With the advent of cyber-physical systems, (physical systems integrated with cyber control layer), human control is no longer constrained to system inputs. Users can also control system outputs through a number of different teleoperation mappings. Given all this flexibility, what is the most intuitive way for a human user to control an arbitrary system and how is intuitiveness quantified? The project focuses on human-in-the-loop control for medical needles, which steer with bicycle-like kinematics. These needles could be used in a variety of medical interventions including tissue biopsy, tumor ablation, abscess drainage, and local drug delivery. We have explored a variety of teleoperation mappings for human control of these steerable needles; yet, we have found inconsistencies between objective performance metrics (e.g., task time and error), and post-experimental surveys on comfort or ease-of use. Users occasionally report a preference for control mappings, which objectively degrade performance, and vice versa. It is important to measure the real-time engagement of the user with the physical system in order to capture the nuances of how different control mappings affect physical effort, mental workload, distraction, drowsiness, and emotional response. Physiological sensors such as electroencephalography (EEG), galvanic skin response (GSR), and electromyography (EMG), can be used to provide these real-time measurements and to quantitatively classify the intuitiveness of new teleoperation algorithms. Broader Impacts: Intuitive and natural human-in-the-loop control interfaces will improve human health and well being, through applications in surgery and rehabilitation. The results of this study will be disseminated publicly on the investigator's laboratory website, a conference workshop, and a new medical robotics seminar to be held jointly between UT Dallas and UT Southwestern Medical Center. Outreach activities, lab tours, and mentoring of underrepresented students at all levels, will broaden participation in STEM. Additionally, the proximity of the investigator?s hospital-based lab to medical professionals will engage non-engineers in design and innovation

This project advances the scientific knowledge on design methods for improving the resilience of civil infrastructures to disruptions. To improve resilience, critical services in civil infrastructure sectors must utilize new diagnostic tools and control algorithms that ensure survivability in the presence of both security attacks and random faults, and also include the models of incentives of human decision makers in the design process. This project will develop a practical design toolkit and platform to enable the integration of resiliency-improving control tools and incentive schemes for Cyber-Physical Systems (CPS) deployed in civil infrastructures. Theory and algorithms will be applied to assess resiliency levels, select strategies to improve performance, and provide reliability and security guarantees for sector-specific CPS functionalities in water, electricity distribution and transportation infrastructures. The main focus is on resilient design of network control functionalities to address problems of incident response, demand management, and supply uncertainties. More broadly, the knowledge and tools from this project will influence CPS designs in water, transport, and energy sectors, and also be applicable to other systems such as supply-chains for food, oil and gas. The proposed platform will be used to develop case studies, test implementations, and design projects for supporting education and outreach activities. Current CPS deployments lack integrated components designed to survive in uncertain environments subject to random events and the actions of strategic entities. The toolkit (i) models the propagation of disruptions due to failure of cyber-physical components, (ii) detects and responds to both local and network-level failures, and (iii) designs incentive schemes that improve aggregate levels of public good (e.g., decongestion, security), while accounting for network interdependencies and private information among strategic entities. The validation approach uses real-world data collected from public sources, test cases developed by domain experts, and simulation software. These tools are integrated to provide a multi-layer design platform, which explores the design space to synthesize solutions that meet resiliency specifications. The platform ensures that synthesized implementations meet functionality requirements, and also estimates the performance guarantees necessary for CPS resilience. This modeling, validation, exploration, and synthesis approach provides a scientific basis for resilience engineering. It supports CPS education by providing a platform and structured workflow for future engineers to approach and appreciate implementation realities and socio-technical constraints.

SCALE2 explores the design of resilient, inexpensive cyber-physical systems (CPS) technologies to create community-wide smartspaces for public/personal safety. SCALE2 aims to demonstrate that community safety can be realized by augmenting CPS technologies with end-to-end resilience mechanisms. Such a study requires real-world community-scale deployments to understand citizen concerns and can only be achieved through partnerships between various stakeholders - researchers, government agencies, and industry. The SCALE2 multisensory platform will use inexpensive Internet of things (IoT) components, and support dependable operation by enabling resilient information-flow through multiple system layers. Research will explore mechanisms for (a) ingest of real-time data through flexible rich data models, (b) Quality of Service (QoS)-aware messaging to cloud platforms, and (c) reliable detection of higher-level community events through semantics-driven virtual sensing. SCALE2, through its established partnerships/testbeds, offers a unique short-term opportunity to guide future resilience technologies, train the next generation of students and have broader community impact. SCALE2 will be deployed at Montgomery County, MD, and the Irvine-Sensorium working with local agencies.

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.

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.

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.