Inadequate system understanding and inadequate situational awareness have caused large-scale power outages in the past. With the increased reliance on variable energy supply sources, system understanding and situational awareness of a complex energy system become more challenging. This project leverages the power of big data analytics to directly improve system understanding and situational awareness. The research provides the methodology for detecting anomalous events in real-time, and therefore allow control centers to take appropriate control actions before minor events develop into major blackouts. The significance for the society and for the power industry is profound. Energy providers will be able to prevent large-scale power outages and reduce revenue losses, and customers will benefit from reliable energy delivery with service guarantees. Students, including women and underrepresented groups, will be trained for the future workforce in this area. The project includes four major thrusts: 1) real-time anomaly detection from measurement data; 2) real-time event diagnosis and interpretation of changes in the state of the network; 3) real-time optimal control of the power grid; 4) scientific foundations underpinning cyber-physical systems. The major outcome of this project is practical solutions to event or fault detection and diagnosis in the power grid, as well as prediction and prevention of large-scale power outages.

Cities provide ready and efficient access to facilities and amenities through shared civil infrastructures such as transportation and healthcare. Making such critical infrastructures resilient to sudden changes, e.g., caused by large-scale disasters, requires careful management of limited and varying resources. The rapidly growing big data from both physical sensors and social media in real-time suggest an unprecedented opportunity for information technology to enable increasing efficiency and effectiveness of adaptive resource management techniques in response to sharp changes in supply and/or demand on critical infrastructures. Within the general areas of resilient infrastructures and big data, this project will focus on the integration of heterogeneous Big Data and real-time analytics that will improve the adaptive management of resources when critical infrastructures are under stress. The integration of heterogeneous data sources is essential because many kinds of physical sensors and social media provide useful information on various critical infrastructures, particularly when they are under stress. This Research Coordination Network (RCN) will promote meetings and activities that stimulate and enable new research on integration of heterogeneous physical sensor data and social media for real-time big data analytics in support of resilient critical infrastructures such as transportation and healthcare in smart cities. As first example, the RCN will support participation from young faculty attending the Early Career Investigators' Workshop on Cyber-Physical Systems in Smart Cities (ECI-CPS) at CPSweek (April of each year) and young faculty attending the Workshop on Big Data Analytics for Cyber-physical Systems (BDACPS). As a second example, the RCN will support contributions to a Special Track on Big Data Analytics for Resilient Infrastructures at the IEEE Big Data Congress. As a third example, the RCN will support participation in International meetings organized by other countries, e.g., Japan's Big Data program by Japan Science and Technology Agency (JST). The project will also maintain a repository of research resources. Concretely, the RCN will actively collect and make readily available public data sets (e.g., physical and social sensor data) and software tools (e.g., to support real-time big data analytics). The technologies and tools that arise from RCN-enabled research will be applied to socially and economically impactful areas such as reducing congestion and personalized healthcare in smart cities.

The objective of this research is to understand the complexities associated with integration between humans and cyber-physical systems (CPS) at large scales. For this purpose, the team will develop and demonstrate the application of Smart City Hubs focusing on intelligent transportation services in urban settings. Ultimately, this project will produce innovative tools and techniques to configure and deploy large-scale scale experiments enabling the study of how humans affect the control loops in large CPS such as smart cities. This work covers several design concerns that are specific to human-CPS such as human computer interfaces, decision support systems and incentives engineering to keep humans engaged with the system. The technology base will include a novel integration platform for allowing (1) integration of spatially and temporally distributed sensor streams; (2) integration of simulation-based decision support systems, (3) development and execution of experiments to understand how advanced decision support tools combined with incentive mechanisms improve the utilization of the transportation infrastructure and user experience. A key aspect of this research will be development of data-driven rider models that can be subsequently used by city engineers for planning purposes. The proposed system will enable a new generation of human-CPS systems where sensing, wireless communication, and data-driven predictive analytics is combined with human decision-making and human-driven actuation (driving and physical infrastructure utilization) to form a control loop. The Smart City Hub provides a generic platform for a number of other services beyond traffic and public transportation, including maps and way finding, municipal communication, emergency management and others. The tools that will be developed will allow researchers and practitioners to more quickly prototype, deploy and experiment with these CPS. To ensure these benefits, the research team will make its research infrastructure freely available as an open-source project. It will also develop educational materials focused on modeling, prototyping and evaluating these applications at scale. In addition, the studies the team will perform will provide new data and new scientific understanding of large-scale human interaction with CPS, which it expects will yield long-term benefits in the design and analysis of such applications.