The electric power grid experiences disturbances all the time that are routinely controlled, managed, or eliminated by system protection measures- designed by careful engineering studies and fine-tuned by condensing years of operational experience. Despite this, the grid sometimes experiences disruptive events that can quickly, and somewhat unstoppably catapult the system towards a blackout. Arresting such blackouts has remained elusive - mainly because relays (protection devices) operate on local data, and are prone to hidden faults that are impossible to detect until they manifest, resulting in misoperations that have sometime been precipitators or contributors to blackouts. Inspired by the Presidential policy directive on resilience -- meaning the ability to anticipate, prepare, withstand, and recover from disruptive events, this project proposes "WARP: Wide Area assisted Resilient Protection", a paradigm that adds a layer of finer (supervisory) intelligence to supplement conventional protection wisdom - which we call resilient protection. Exploiting high fidelity measurements and computation to calculate and analyze energy function components of power systems to identify disturbances, WARP would allow relays to be supervised - correct operations would be corroborated, and misoperations will be remedied by judiciously reversing the relay operation in a rational time-frame. The project also envisions predicting instability using advanced estimation techniques, thus being proactive. This will provide power grid the ability to auto-correct and bounce back from misoperations, curtailing the size, scale and progression of blackouts and improving the robustness and resilience of the electric grid -- our nation's most critical infrastructure. In WARP, disruptive events are deciphered by using synchrophasor data, energy functions, and dynamic state information via particle filtering. The information is fused to provide a global data set and intelligence signal that supervises relays, and also to predict system stability. Resilience is achieved when the supervisory signal rectifies the misoperation of relays, or endorses their action when valid. This endows relays with post-event-auto-correct abilities 
- a feature that never been explored/understood in the protection-stability nexus. Architectures to study the effect of latency and bad data are proposed. WARP introduces new notions: global detectability and distinguishability for power system events, stability prediction based on
the sensitivity of the energy function components and uses a novel factorization method: (CUR) preserving data interpretability to reduce data dimensionality. All the proposed
tools will be wrapped into a simulation framework to assess scalability and accuracy-runtime tradeoffs, and quantify the degree of resilience achieved. The effectiveness of the proposed scheme during extreme events will be measured by reenacting two well-documented blackout sequences. In addition, simulations on benchmarked systems will be performed to assess scalability and accuracy-runtime tradeoffs, and quantify the degree of resilience achieved.

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).

Errors in cyber-physical systems can lead to disastrous consequences. Classic examples date back to the Therac-25 radiation incidents in 1987 and the Ariane 5 rocket crash in 1996. More recently, Toyota's unintended acceleration bug was caused by software errors, and certain cars were found vulnerable to attacks that can take over key parts of the control software, allowing attackers to even disable the brakes remotely. Pacemakers have also been found vulnerable to attacks that can cause deadly consequences for the patient. To reduce the chances of such errors happening, this project investigates the application of a technique called Foundational Verification to cyber-physical systems. In Foundational Verification, the system being developed is proved correct, in full formal detail, using a proof assistant. The main intellectual merit of the proposal is the attainment of previously unattainable levels of safety for cyber-physical systems because proofs in Foundational Verification are carried out in complete detail. To ensure that the techniques in this project are practical, they are evaluated within the context of a real flying quadcopter. The project's broader significance and importance is the improved correctness, safety and security of cyber-physical systems. In particular, this project lays the foundation for ushering in a new level of formal correctness for cyber-physical systems. Although the initial work focuses on quadcopters, the concepts, ideas, and research contributions have the potential for transformative impact on other kinds of systems, including power-grid software, cars, avionics and medical devices (from pacemakers and insulin pumps to defibrillators and radiation machines).

Production as a service (PaaS) defines a new paradigm in manufacturing that will allow designers of new products to query existing manufacturing facilities and receive information about fabrication capabilities and production availability. The access to information such as part cost, part quality, and production time will help new products to be prototyped and scaled-up quickly, while also allowing existing manufacturing facilities to benefit from underutilized equipment and labor. The PaaS framework will include both a front-end query interface for the users and a back-end analysis component. The interface will be designed to connect users with small-, mid-, and large-sized manufacturing facilities, while the scheduling and routing algorithms will provide the flexibility and security protocols needed to guarantee operational and production safety across the range of facilities. Manufacturers that utilize the PaaS framework will reap the potential of meeting customer needs in terms of cost, quality, on-time delivery, while being reactive to changing market forces. With 12 percent of the GDP represented by the manufacturing industry, the manufacturing operational improvements that will result from this EArly-concept Grant for Exploratory Research (EAGER) project have the potential to make a significant impact in the national bottom line. The aim of the PaaS platform is to enable distributed manufacturing plant locations to efficiently coordinate both within one plant location as well as across plant locations to realize a flexible service interface for supporting production management. The intellectual merit of this research lies in the extensions that will be created to the existing science and technology in service-oriented architectures to enable distributed production, while preserving proprietary information of the manufacturing systems. The key software abstraction that enables this innovation comes from the extension to the well-known APIs to capture the sophisticated query logic and diverse production requirements to meet user needs. Routing and scheduling decisions will be optimized by leveraging a global view of the current state of all of the components in the manufacturing facilities. To demonstrate scalability and ensure privacy guarantees across multiple facilities, hierarchical abstraction will be used to hide low-level details and proprietary information. The PaaS framework will transform the way manufacturing companies interact with the emerging high-value market; providing the architecture to drive innovation and enable small-, mid-, and large-scale manufacturing companies across the U.S. to compete for new product business on an even playing field.

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.

Laboratory-on-a-chip (LoC) technology is poised to improve global health through development of low-cost, automated point-of-care testing devices. In countries with few healthcare resources, clinics often have drugs to treat an illness, but lack diagnostic tools to identify patients who need them. To enable low-cost diagnostics with minimal laboratory support, this project will investigate domain-specific LoC programming language and compiler design in conjunction with device fabrication technologies (process flows, sensor integration, etc.). The project will culminate by building a working LoC that controls fluid motion through electronic signals supplied by a host PC; a forensic toxicology immunoassay will be programmed in software and executed on the device. This experiment will demonstrate benefits of programmable LoC technology including miniaturization (reduced reagent consumption), automation (reduced costs and uncertainties associated with human interaction), and general-purpose software-programmability (the device can execute a wide variety of biochemical reactions, all specified in software). Information necessary to reproduce the device, along with all software artifacts developed through this research effort, will be publicly disseminated. This will promote widespread usage of software-programmable LoC technology among researchers in the biological sciences, along with public and industrial sectors including healthcare and public health, biotechnology, water supply management, environmental toxicity monitoring, and many others. This project designs and implements a software-programmable cyber-physical laboratory-on-a-chip (LoC) that can execute a wide variety of biological protocols. By integrating sensors during fabrication, the LoC obtains the capability to send feedback in real-time to the PC controller, which can then make intelligent decisions regarding which biological operations to execute next. To bring this innovative and transformative platform to fruition, the project tackles several formidable research challenges: (1) cyber-physical LoC programming models and compiler design; (2) LoC fabrication, including process flows and cyber-physical sensor integration; and (3) LoC applications that rely on cyber-physical sensory feedback and real-time decision-making. By constructing a working prototype LoC, and programming a representative feedback-driven forensic toxicology immunoassay, the project demonstrates that the proposed system can automatically execute biochemical reactions that require a closed feedback loop. Expected broader impacts of the proposed work include reduced cost and increased reliability of clinical diagnostics, engagement with U.S. companies that use LoC technology, training of graduate and undergraduate students, increased engagement and retention efforts targeting women and underrepresented minorities, student-facilitated peer-instruction at UC Riverside, a summer residential program for underrepresented minority high-school students at the University of Tennessee, collaborations with researchers at the Oak Ridge National Laboratory, and creation, presentation, and dissemination of tutorial materials to promote the adoption and use of software-programmable LoCs among the wider scientific community.

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.