Data-driven intelligence is an essential foundation for physical systems in transportation safety and efficiency, area surveillance and security, as well as environmental sustainability. This project develops new computer system infrastructure and algorithms for self-sustainable data-driven systems in the field. Research outcomes of the project include (a) a low-maintenance, environmentally-friendly hardware platform with solar energy harvesting and super capacitor-based energy storage, (b) virtualization software infrastructure for low-power nodes to enable inter-operability among distributed field nodes and from/to the data center, and (c) new image and data processing approaches for resource-adaptive fidelity adjustment and function partitioning. The synergy between the self-sustainable hardware, system software support, wireless communications management, and application data processing manifests through global coordination for quality-of-service, energy efficiency, and data privacy. In broader impacts, this project enables data-driven intelligence in the field for important physical system domains. Integration of the technologies involved is accomplished through real-world system deployment and experimentation, including an intelligent campus traffic and parking management system and collaborative work with industry collaborators. The results of this project will further enhance the technological competitiveness for US industries in key areas such as intelligent transportation. The education component includes cross-disciplinary curriculum enhancements and the development of a new instructional platform for realistic experiments with cyber-physical systems. Within the scope of this project, the PIs perform mentoring and outreach activities to recruit/retain women and minorities in science and engineering.

Energy infrastructure is a critical underpinning of modern society. To ensure its reliable operation, a nation-wide or continent-wide situational awareness system is essential to provide high-resolution understanding of the system dynamics such that proper actions can be taken in real-time in response to power system disturbances and to avoid cascading blackouts. The power grid represents a typical highly dynamic cyber-physical system (CPS). The ever-increasing complexity and scale in sensing and actuation, compounded by the limited knowledge of the accurate system state have resulted in major system failures, such as the massive power blackout of August 2003 and the most recent Arizona/California blackout of September 2011. Therefore, methods and tools for monitoring and control of these and other such dynamic systems at high resolution are vital to an emergent generation of tightly coupled, physically distributed CPS. This project employs the power grid as a target application and develops a high-resolution, ultra-wide-area situational awareness system that synergistically integrates sensing, processing, and actuation. First, from the sensing perspective, high resolution is reflected in both measurement accuracy and potential for dense spatial coverage. Wide area, precise, synchronized, and affordable sensing in voltage angle and frequency measurements for large-scale observation is sorely needed to observe system disturbances and capture critical changes in the power grid. The crucial innovation of this work is to make accurate frequency measurement from low voltage distribution systems through the wide deployment of Frequency Disturbance Recorders (FDRs). Second, from a data processing perspective, high resolution is reflected in finer-scale data analysis to reveal hidden information. In practical CPS, events seldom occur in an isolated fashion; cascading events are more common and realistic. A new conceptual framework is presented in the study of event analysis, referred to as event unmixing, where real-world events are considered a mixture of more than one constituent root event. This concept is a key enabler for the analysis of events to go beyond what are immediately detectable in the system. The event formation process is interpreted from a linear mixing perspective and innovative sparsity-constrained unmixing algorithms are presented for multiple event separation and spatial-temporal localization. Third, to discover the high-level spatial-temporal correlation among root events in real time, a descriptive language is developed to discover patterns on the spatial and temporal information of root events. This descriptive language allows embedding pattern descriptions on the desirable and undesirable interactions between events in the system, which will then be compiled into distributed runtime constructs to be executed in deployed systems. Fourth, from the actuation perspective, the system pushes the intelligence toward the lower level of the power grid allowing local devices to make decisions and to react quickly to contingencies based on the high-resolution understanding of the system state, enabling a more direct reconfiguration of the physical makeup of the grid. Finally, the methods and tools are implemented and validated on an existing wide-area power grid monitoring system, the North American frequency monitoring network (FNET). Escalating demands for electricity coupled with an outdated power transmission grid pose a serious threat to the US economy. The transformative nature of this research is to turn a large volume of real-time data into actionable information and help prevent potential outages from happening. The power grid is a typical example of dynamic cyber physical system. Providing high-resolution situational awareness for the power grid has a direct and immediate impact on this and other CPS. The research is coupled with a strong educational component including active recruitment of students from underrepresented groups supported by existing programs and broad dissemination of research findings.

Continuous real-time tracking of the eye and field-of-view of an individual is profoundly important to understanding how humans perceive and interact with the physical world. This work advances both the technology and engineering of cyber-physical systems by designing an innovative paradigm involving next-generation computational eyeglasses that interact with a user's mobile phone to provide the capability for real-time visual context sensing and inference. This research integrates novel research into low-power embedded systems, image representation, image processing and machine learning, and mobile sensing and inference, to advance the state-of-art in continuous sensing for CPS applications. The activity addresses several fundamental research challenges including: 1) design of novel, highly integrated, computational eyeglasses for tracking eye movements, the visual field of a user, and head movement patterns, all in real-time; 2) a unified compressive signal processing framework that optimizes sensing and estimation, while enabling re-targeting of the device to perform a broad range of tasks depending on the needs of an application; 3) design of a novel real-time visual context sensing system that extracts high-level contexts of interest from compressed data representations; and 4) a layer of intelligence that combines contexts extracted from the computational eyeglass together with contexts obtained from the mobile phone to improve energy-efficiency and sensing accuracy. This technology can revolutionize a range of disciplines including transportation, healthcare, behavioral science and market research. Continuous monitoring of the eye and field-of-view of an individual can enable detection of hazardous behaviors such as drowsiness while driving, mental health issues such as schizophrenia, addictive behavior and substance abuse, neurological disease progression, head injuries, and others. The research provides the foundations for such applications through the design of a prototype platform together with real-time sensor processing algorithms, and making these systems available through open source venues for broader use. Outreach for this project includes demonstrations of the device at science fairs for high-school students, and integration of the platform into undergraduate and graduate courses.

This project explores balancing performance considerations and power consumption in cyber-physical systems, through algorithms that switch among different modes of operation (e.g., low-power/high-power, on/off, or mobile/static) in response to environmental conditions. The main theoretical contribution is a computational, hybrid optimal control framework that is connected to a number of relevant target applications where physical modeling, control design, and software architectures all constitute important components. The fundamental research in this program advances state-of-the-art along four different dimensions, namely (1) real-time, hybrid optimal control algorithms for power management, (2) power-management in mobile sensor networks, (3) distributed power-aware architectures for infrastructure management, and (4) power-management in embedded multi-core processors. The expected outcome, which is to enable low-power devices to be deployed in a more effective manner, has implications on a number of application domains, including distributed sensor and communication networks, and intelligent and efficient buildings. The team represents both a research university (Georgia Institute of Technology) and an undergraduate teaching university (York College of Pennsylvania) in order to ensure that the educational components are far-reaching and cut across traditional educational boundaries. The project involves novel, inductive-based learning modules, where graduate students team with undergraduate researchers.

The project aims at making cities "smarter" by engineering processes such as traffic control, efficient parking services, and new urban activities such as recharging electric vehicles. To that end, the research will study the components needed to establish a Cyber-Physical Infrastructure for urban environments and address fundamental problems that involve data collection, resource allocation, real-time decision making, safety, and security. Accordingly, the research is organized along two main directions: (i) Sensing and data acquisition using a new mobile sensor network paradigm designed for urban environments; and (ii) Decision Support for the "Smart City" relying on formal verification and certification methods coupled with innovative dynamic optimization techniques used for decision making and resource allocation. The work will bring together and build upon methodological advances in optimization under uncertainty, computer simulation, discrete event and hybrid systems, control and games, system security, and formal verification and safety. Target applications include: a "Smart Parking" system where parking spaces are optimally assigned and reserved, and vehicular traffic regulation. The research has the potential of revolutionizing the way cities are viewed: from a passive living and working environment to a highly dynamic one with new ways to deal with transportation, energy, and safety. Teaming up with stakeholders in the Boston Back Bay neighborhood, the City of Boston, and private industry, the research team expects to establish new collaborative models between universities and urban groups for cutting-edge research embedded in the deployment of an exciting technological, economic, and sociological development.

Large-scale critical infrastructure systems, including energy and transportation networks, comprise millions of individual elements (human, software and hardware) whose actions may be inconsequential in isolation but profoundly important in aggregate. The focus of this project is on the coordination of these elements via ubiquitous sensing, communications, computation, and control, with an emphasis on the electric grid. The project integrates ideas from economics and behavioral science into frameworks grounded in control theory and power systems. Our central construct is that of a ?resource cluster,? a collection of distributed resources (ex: solar PV, storage, deferrable loads) that can be coordinated to efficiently and reliably offer services (ex: power delivery) in the face of uncertainty (ex: PV output, consumer behavior). Three topic areas form the core of the project: (a) the theoretical foundations for the ?cluster manager? concept and complementary tools to characterize the capabilities of a resource cluster; (b) centralized resource coordination strategies that span multiple time scales via innovations in stochastic optimal control theory; and (c) decentralized coordination strategies based on cluster manager incentives and built upon foundations of non-cooperative dynamic game theory. These innovations will improve the operation of infrastructure systems via a cyber-physical-social approach to the problem of resource allocation in complex infrastructures. By transforming the role of humans from passive resource recipients to active participants in the electric power system, the project will facilitate energy security for the nation, and climate change mitigation. The project will also engage K-12 students through lab-visits and lectures; address the undergraduate demand for power systems training through curricular innovations at the intersection of cyber-systems engineering and physical power systems; and equip graduate students with the multi-disciplinary training in power systems, communications, control, optimization and economics to become leaders in innovation.