Today's automobiles are increasingly autonomous. The latest Mercedes S-class sedan applies corrective action when its driver strays out of lane or tailgates too closely. Semi-autonomy will soon yield to full autonomy. Nissan has promised a line of self-driving cars by 2020. Maritime craft are likewise moving from rudimentary autopilots to full autonomy, and autonomous aerial vehicles will doubtless play a significant role in the future economy. Current versions of these vehicles are cocooned in an array of sensors, but neither the sensors nor the timing, navigation, and collision avoidance algorithms they feed have been designed for security against malicious attacks. Radar and acoustic sensors transmit predictable, uncoded signals; vehicle-to-vehicle communication protocols are either unauthenticated or critically dependent on insecure civil GPS signals (or both); and vehicle state estimators are designed for robustness but not security. These vulnerabilities are not merely conceptual: GPS spoofing attacks have been demonstrated against a drone and an ocean vessel, causing the drone to crash and the vessel to veer off course; likewise, it appears possible to cause road accidents by fooling a car's radar sensor into thinking a crash is imminent, thus triggering automatic braking. This proposal seeks funding to fix these vulnerabilities by developing sensors and high-level decision-making algorithms that are hardened against such so-called field attacks. The goal of secure control systems is to survive and operate safely despite sensor measurements or control commands being compromised. This proposal focuses on an emergent category of cyber-physical attack that has seen little scrutiny in the secure control literature. Like cyber attacks, these attacks are hard to detect and can be executed from a distance, but unlike cyber attacks, they are effective even against control systems whose software, data, and communications networks are secure, and so can be considered a more menacing long-term threat. These are attacks on the physical fields such as electromagnetic, magnetic, acoustic, etc. measured by system sensors. As specialized sensor attacks, field attacks seek to compromise a system's perception of reality non-invasively from without, not from within. We emphasize field attacks against navigation, collision avoidance, and synchronization sensors, as these are of special importance to the rise of autonomous vehicles and the smart grid. This proposal's goal is to develop a coherent analytical foundation for secure perception in the presence of field attacks and to develop a suite of algorithms and tools to detect such attacks. A key insight behind this proposal's approach is that the physics of field attacks impose fundamental difficulties on the attacker that can be exploited and magnified to enable attack detection. This work will progressively build security into navigation, collision avoidance, and timing perception from the physical sensory layer to the top-level state estimation algorithms. The outcome of this work will be smarter, more skeptical sensor systems for autonomous vehicles and other autonomous systems.

This project addresses urgent challenges in high confidence validation and verification of automotive vehicles due to on-going and anticipated introduction of advanced, connected and autonomous vehicles into mass production. Since such vehicles operate across both physical and cyber domains, faults can occur in traditional physical components, in cyber components (i.e., algorithms, processors, networks, etc.), or in both. Thus, advanced vehicles need to be tested for both physical and cyber-related fault conditions. The goal of this project is to develop theory, methods, and novel tools for generating and optimizing test trajectories and data inputs that can uncover both physical and cyber faults of future automotive vehicles. The level of vehicle reliability and safety achieved for current vehicles is remarkable considering their mass production, low cost, and wide range of operating conditions. If successful, the research advances made in this project will enable achieving similar levels of reliability and safety for future vehicles relying on advanced driver assistance technologies, connectivity and autonomy. The project will advance the field of cyber-physical systems, in general, and their lifecycle management, in particular. The validation and verification theory and methodology for cyberphysical systems will be expanded for uncovering anomalies and faults, especially using comprehensive case-based and optimization-based techniques for test scenario generation. The theoretical advances and case studies will contribute to the state-of-the-art in optimal control theory, game theory, information theory, data collection and processing, autonomous and connected vehicles, and automotive control. Sampling-based vehicle data acquisition and vehicle-aware data management strategies will be developed which can be applied more broadly, e.g., to cloud-based vehicle prognostics / conditional maintenance and mobile health-monitoring devices. Finally, approaches for efficient on-board data collection and aggregation will be implemented in a Cyber-physical system (CPS) Black Box prototype. The development of a vehicle-aware data management system (VDMS) will be pursued, leading to optimized use of data mining and compression inside the CPS Black Box to aggressively reduce the communication and computational costs. Synergistically with theoretical and methodological advances, automotive case studies will be undertaken with both realistic simulations and real experiments in collaboration with an industrial partner (AVL).

This project develops advanced cyber-physical sensing, modeling, control, and optimization methods to significantly improve the efficiency of algal biomass production using membrane bioreactor technologies for waste water processing and algal biofuel. Currently, many wastewater treatment plants are discharging treated wastewater containing significant amounts of nutrients, such as nitrogen, ammonium, and phosphate ions, directly into the water system, posing significant threats to the environment. Large-scale algae production represents one of the most promising and attractive solutions for simultaneous wastewater treatment and biofuel production. The critical bottleneck is low algae productivity and high biofuel production cost. The previous work of this research team has successfully developed an algae membrane bioreactor (A-MBR) technology for high-density algae production which doubles the productivity in an indoor bench-scale environment. The goal of this project is to explore advanced cyber-physical sensing, modeling, control, and optimization methods and co-design of the A-MBR system to bring the new algae production technology into the field. The specific goal is to increase the algal biomass productivity in current practice by three times in the field environment while minimizing land, capital, and operating costs. Specifically, the project will (1) adapt the A-MBR design to address unique new challenges for algae cultivation in field environments, (2) develop a multi-modality sensor network for real-time in-situ monitoring of key environmental variables for algae growth, (3) develop data-driven knowledge-based kinetic models for algae growth and automated methods for model calibration and verification using the real-time sensor network data, and (4) deploy the proposed CPS system and technologies in the field for performance evaluations and demonstrate its potentials. This project will demonstrate a new pathway toward green and sustainable algae cultivation and biofuel production using wastewater, addressing two important challenging issues faced by our nation and the world: wastewater treatment and renewable energy. It will provide unique and exciting opportunities for mentoring graduate students with interdisciplinary training opportunities, involving K-12 students, women and minority students. With web-based access and control, this project will convert the bench-scale and pilot scale algae cultivation systems into an exciting interactive online learning platform to educate undergraduate and high-school students about cyber-physical system design, process control, and renewable biofuel production.

In the recent past the term "Smart Cities" was introduced to mainly characterize the integration into our daily lives of the latest advancements in technology and information. Although there is no standardized definition of Smart Cities, what is certain is that it touches upon many different domains that affect a city's physical and social capital. Smart cities are intertwined with traffic control systems that use advanced infrastructures to mitigate congestion and improve safety. Traffic control management strategies have been largely focused on improving vehicular traffic flows on highways and freeways but arterials have not been used properly and pedestrians are mostly ignored. This work proposes to introduce a novel hierarchical adaptive controls paradigm to urban network traffic control that will adapt to changing movement and interaction behaviors from multiple entities (vehicles, public transport modes, bicyclists, and pedestrians). Such a paradigm will leverage several key ideas of cyber-physical systems to rapidly and automatically pin-point and respond to urban arterial congestion thereby improving travel time and reliability for all modes. Safety will also be improved since advanced warnings actuated by the proposed cyber-physical system will alert drivers to congested areas thereby allowing them to avoid these areas, or to adapt their driving habits. Such findings have a tangible effect on the well-being, productivity, and health of the traveling public. The primary goal is to create a Cyber-Control Network (CCN) that will integrate seamlessly across heterogeneous sensory data in order to create effective control schemes and actuation sequences. Accordingly, this project introduces a Cyber-Physical architecture that will then integrate: (i) a sub-network of heterogeneous sensors, (ii) a decision control substrate, and (iii) a sub-actuation network that carries out the decisions of the control substrate (traffic control signals, changeable message signs). This is a major departure from more prevalent centralized Supervisory Control And Data Acquisition (SCADA), in that the CCN will use a hierarchical architecture that will dynamically instantiate the sub-networks together to respond rapidly to changing cyber-physical interactions. Such an approach allows the cyber-physical system to adapt in real-time to salient traffic events occurring at different scales of time and space. The work will consequently introduce a ControlWare module to realize such dynamic sub-network reconfiguration and provide decision signal outputs to the actuation network. A secondary, complementary goal is to develop a heterogeneous sensor network to reliably and accurately monitor and identify salient arterial traffic events. Other impacts of the project include the integration of the activities with practitioners (e.g., traffic engineers), annual workshops/tutorials, and outreach to K-12 institutions.

Parking can take up a significant amount of the trip costs (time and money) in urban travel. As such, it can considerably influence travelers' choices of modes, locations, and time of travel. The advent of smart sensors, wireless communications, social media and big data analytics offers a unique opportunity to tap parking's influence on travel to make the transportation system more efficient, cleaner, and more resilient. A cyber-physical social system for parking is proposed to realize parking's potential in achieving the above goals. This cyber-physical system consists of smart parking sensors, a parking and traffic data repository, parking management systems, and dynamic traffic flow control. If successful, the results of the investigation will create a new paradigm for managing parking to reduce traffic congestion, emissions and fuel consumption and to enhance system resilience. These results will be disseminated broadly through publications, workshops and seminars. The research will provide interdisciplinary training to both graduate and undergraduate students. The results of this research also fills a void in our graduate transportation curriculum in which parking management gets little coverage. The investigators will organize an online short training course in Coursera and National Highway Institute to bring results to a broader audience. The investigators will also collaborate with Carnegie Museum of Natural History to develop an online digital map and related educational programs, which will be presented in the museum galleries during public events. Technically, new theories, algorithms and systems for efficient management of transportation infrastructure through parking will be developed in this research, leveraging cutting-edge sensing technology, communication technology, big data analytics and feedback control. The research probes massive individualized and infrastructure based traffic and parking data to gain a deeper understanding of travel and parking behavior, and develops a novel reservoir-based network flow model that lays the foundation for modeling the complex interactions between parking and traffic flow in large-scale transportation networks. The theory will be investigated at different levels of granularity to reveal how parking information and pricing mechanisms affect network flow in a competitive market of private and public parking. In addition, this research proposes closed-loop control mechanisms to enhance mobility and sustainability of urban networks. Prices, access and information of publicly owned on-street and off-street parking are dynamically controlled to: a) change day-to-day behavior of all commuters through day-to-day travel experience and/or online information systems; b) change travel behavior of a fraction of adaptive travelers on the fly who are aware of time-of-day parking information and comply to the recommendations; and c) influence the market prices of privately owned parking areas through a competitive parking market.

Electricity usage of buildings (including offices, malls and residential apartments) represents a significant portion of a nation's energy expenditure and carbon footprint. Buildings are estimated to consume 72% of the total electricity production in the US. Unfortunately, however, 30% of this energy consumption is wasted. Virtual energy assessment is an approach that can optimize building energy efficiency and minimize waste at a low cost with minimal expert intervention. A virtual energy audit includes a thorough and near real time analysis of different sources of building energy usage, individualized energy footprints of load appliances and devices, and proactive identification of energy holes and air leakages. This project builds a low cost solution that combines the use of non-intrusive single point energy monitoring and low cost IR cameras to provide continuous energy audits. The system will provide continuous virtual audit reports to the landlords or residential users. The system will be deployed in low-income neighborhoods in Baltimore City, Maryland, where poor insulation problems are assumed to be fiscally insurmountable and low cost solutions to determining these issues is important for the landlords. To develop a scalable low cost virtual energy auditing system, this breakthrough research pursues the interfaces of smart building sensing, computing and actuation. The project will be executed under three main research thrust areas. First, it utilizes an autonomous discovery, profiling and rule-based predictive model to capture the relationship between disaggregated power measures and a device's actual usage patterns to pinpoint any abnormal consumption. Second, the PIs develop zero-energy far-infrared imaging sensors for low cost low frequency heat map scanning and air leakage detection. Third, the project engineers and evaluates cyber-physical building sensing system with a control level design perspective for virtual energy auditing that drives the realization of deep energy savings and building efficiency. Additionally, the PIs with collaboration from Constellation will host building energy education projects and workshop where undergraduate, high school, and underrepresented group of students would understand how to design and program energy meters and smart plugs.

Cells, to carry out many important functions, employ an elaborate transport network with bio-molecular components forming roadways as well as vehicles. The transport is achieved with remarkable robustness under a very uncertain environment. The main goal of this proposal is to understand how biology achieves such functionality and leveraging the knowledge toward realizing effective engineered transport mechanisms for micron sized cargo. The realization of a robust infrastructure that enables simultaneous transport of many micron and smaller sized particles will have a transformative impact on a vast range of areas such as medicine, drug development, electronics, and bio-materials. A key challenge here is to probe the mechanisms often at the nanometer scale as the bio-molecular components are at tens of nanometer scale. The main tools for addressing these challenges come from an engineering perspective that is guided by existing insights from biology. The proposal will bring together researchers from engineering and biology and it provides an integrated environment for students. Moreover, it is known that an impaired transport mechanism can underlie many neurodegenerative maladies, and as the research here pertains to studying intracellular transport, discoveries hold the potential for shedding light on what causes the impaired transport. Robust infrastructure that enables simultaneous transport of many micron and smaller sized particles will have a transformative impact on a vast range of areas such as medicine, drug development, electronics, and bio-materials. Daunting challenges from the underlying highly uncertain and complex environments impede enabling robust and efficient transport systems at micro-scale. Motivated by transport in biological cells, this work proposes a robust and efficient engineered infrastructure for transporting micron/molecular scale cargo using biological constructs. For probing and manipulating the transport network, the proposal envisions strategies for coarse and fine resolution objectives at the global and local scales respectively. At the fine scale of monitoring and control, scarce and expensive physical resources such as high resolution sensors have to be shared for interrogation/control of multiple carriers. In this proposal, the principles for joint control, sensor allocation and scheduling of resources to achieve enhanced performance objectives of a high resolution probing tool, will be developed. A modern control perspective forms an essential strategy for managing multiple objectives. At the global scale, entire traffic will be monitored to arrive at real-time and off-line inferences on traffic modalities. Associated principles for dynamically identifying and tracking clusters of carriers and their importance will be built. This categorization of physical elements and their importance will determine the dynamic allocation of computational resources. Associated study of trade-offs will guide a combined strategy for allocation of computational resources and gathering of information on physical elements. Methods based on the reconstruction of graph topologies for reaching inferences that are suited to dynamically related time trajectories for the transportation infrastructure will be developed. The research proposed is transformative as it will enable a new transport paradigm at the cellular scale, which will also provide unique insights into intracellular transport where it will be possible to investigate multiple factors under the same experimental conditions.

Assistive machines - like powered wheelchairs, myoelectric prostheses and robotic arms - promote independence and ability in those with severe motor impairments. As the state- of-the-art in these assistive Cyber-Physical Systems (CPSs) advances, more dexterous and capable machines hold the promise to revolutionize ways in which those with motor impairments can interact within society and with their loved ones, and to care for themselves with independence. However, as these machines become more capable, they often also become more complex. Which raises the question: how to control this added complexity? A new paradigm is proposed for controlling complex assistive Cyber-Physical Systems (CPSs), like robotic arms mounted on wheelchairs, via simple low-dimensional control interfaces that are accessible to persons with severe motor impairments, like 2-D joysticks or 1-D Sip-N-Puff interfaces. Traditional interfaces cover only a portion of the control space, and during teleoperation it is necessary to switch between different control modes to access the full control space. Robotics automation may be leveraged to anticipate when to switch between different control modes. This approach is a departure from the majority of control sharing approaches within assistive domains, which either partition the control space and allocate different portions to the robot and human, or augment the human's control signals to bridge the dimensionality gap. How to best share control within assistive domains remains an open question, and an appealing characteristic of this approach is that the user is kept maximally in control since their signals are not altered or augmented. The public health impact is significant, by increasing the independence of those with severe motor impairments and/or paralysis. Multiple efforts will facilitate large-scale deployment of our results, including a collaboration with Kinova, a manufacturer of assistive robotic arms, and a partnership with Rehabilitation Institute of Chicago. The proposal introduces a formalism for assistive mode-switching that is grounded in hybrid dynamical systems theory, and aims to ease the burden of teleoperating high-dimensional assistive robots. By modeling this CPS as a hybrid dynamical system, assistance can be modeled as optimization over a desired cost function. The system's uncertainty over the user's goals can be modeled via a Partially Observable Markov Decision Processes. This model provides the natural scaffolding for learning user preferences. Through user studies, this project aims to address the following research questions: (Q1) Expense: How expensive is mode-switching? (Q2) Customization Need: Do we need to learn mode-switching from specific users? (Q3) Learning Assistance: How can we learn mode-switching paradigms from a user? (Q4) Goal Uncertainty: How should the assistance act under goal uncertainty? How will users respond? The proposal leverages the teams shared expertise in manipulation, algorithm development, and deploying real-world robotic systems. The proposal also leverages the teams complementary strengths on deploying advanced manipulation platforms, robotic motion planning and manipulation, and human-robot comanipulation, and on robot learning from human demonstration, control policy adaptation, and human rehabilitation. The proposed work targets the easier operation of robotic arms by severely paralyzed users. The need to control many degrees of freedom (DoF) gives rise to mode-switching during teleoperation. The switching itself can be cumbersome even with 2- and 3-axis joysticks, and becomes prohibitively so with more limited (1-D) interfaces. Easing the operation of switching not only lowers this burden on those already able to operate robotic arms, but may open use to populations to whom assistive robotic arms are currently inaccessible. This work is clearly synergistic: at the intersection of robotic manipulation, human rehabilitation, control theory, machine learning, human-robot interaction and clinical studies. The project addresses the science of CPS by developing new models of the interaction dynamics between the system and the user, the technology of CPS by developing new interfaces and interaction modalities with strong theoretical foundations, and the engineering of CPS by deploying our algorithms on real robot hardware and extensive studies with able-bodied and users with sprinal cord injuries.

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.

Epilepsy is one of the most common neurological disorders, affecting between 0.4% and 1% of the world's population. While seizures can be controlled in approximately two thirds of newly diagnosed patients through the use of one or more antiepileptic drugs (AEDs), the remainder experience seizures even on multiple medications. The primary impacts of the chronic condition of epilepsy on a patient are a lower quality of life, loss of productivity, comorbidities, and increased risk of death. Epilepsy is an intermittent brain disorder, and in localization-related epilepsy, which is the most common form of epilepsy, one or a few discrete brain areas (the seizure focus or seizure foci) are believed to be responsible for seizure initiation. More recent approaches with implantable electrical stimulation seizure control devices hold value as a therapeutic option for the control of seizures. These devices, directly or indirectly, target the seizure focus and seek to control its expression. In this project we will build a multichannel brain implantable device based on emerging cyber physical system (CPS) principles. This brain implantable CPS device will incorporate key design features to make the device dependable, scalable, composable, certifiable, and interoperable. The device will operate over the life of an animal, or a patient, and continuously record brain activity and stimulate the brain when seizure related activity is detected to abort an impending seizure. Episodic brain disorders such as epilepsy have a considerable impact on a patient's productivity and quality of life and may be life-threatening when seizures cannot be controlled with medications. The goal of this project is to create a second generation brain-implantable sensing and stimulating device (BISSD) based on emerging CPS principles and practice. The development of a BISSD as a exemplifies several defining aspects that inform and illustrate core CPS principles. First, to meet the important challenge of regulatory approval a composable, scalable and certifiable framework that supports testing in multiple species is proposed. Second, a BISSD must be wholly integrated with the patient and fully cognizant at every instant of brain state, including dynamic changes in both the normal and abnormal expression of brain physiology and therapeutic intervention. Thus, this project seeks a tight conjunction of the cyber solution that must monitor itself and monitor and stimulate the brain using implanted, adaptable, distributed, and networked electrodes, and the physical system which in this case is the intermittently failing human brain. Third, a BISSD must function for an extensive period of time, up to the life of the patient, because each surgery to place and retrieve a BISSD carries an attendant risk. This requirement necessitates a dependable solution, which this project seeks to reliably achieve through both an understanding of the brain's foreign body response and a unique hierarchical fault-tolerant design. Fourth, an advanced salient approaches to acquire, compress, and analyze sensor signals to achieve real-time monitoring and control of seizures is employed. This project should yield a powerful, scalable CPS framework for robust fault-tolerant implantable medical devices with real-time processing that can grow with advances in sensors, sensing modalities, time-series analysis, real-time computation, control, materials, power and knowledge of underlying biology. The USA has a competitive advantage in the control of seizures in medically refractory epilepsy. In the modern era, epilepsy surgery evolved in the USA in the 1970s and spread from here to other parts of the world. Similarly, the USA enjoys a competitive advantage in BISSDs, and success in this effort will enable the USA to build on and maintain this advantage. In addition to epilepsy, advances made here can be expected to benefit the treatment of other neurological and psychiatric brain disorders.