This project develops an integrated framework of communications, computation and control for understanding wide-area power system performance in the face of unpredictable disturbances. The power system is chosen as a particularly challenging cyber physical system (CPS) due to its extreme dimension, geographic reach and high reliability requirements. The following tasks are studied in the proposed research: (a) a Partial Difference Equation (PdE) framework to model the impact of network topology on the power system stability; (b) the design of a communication network for CPS, based on the PdE modeling;(c) the design of a control system, which addresses the challenges such as fast response and resource constraints; (d) the design of a computing infrastructure, which addresses the computation for controlling the power network, in particular, the communication complexity for controlling the power network in both cases of one-snapshot computation and iterative computations; and (e) the test and evaluation for both small scale system models of several hundred buses and very large system models of ~50,000 buses. This work contributes to the broader understanding of CPS with high reliability requirements, particularly, critical infrastructures such as the power grid. Modern infrastructures are complex systems of communications and computation tied to the controls of the physical system. The proposed research contributes to improved reliability by addressing the propagation of disturbances and advancing the understanding of geographically distributed CPS. The PIs plan to open multiple courses on CPS related to the proposed research.

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.

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.

This project will construct a wireless network of animal-borne embedded devices that will be deployed and tested in a biologically-relevant application. The networked devices will provide not only geo-location data, but also execute cooperative strategies that save battery-life by selectively recording bandwidth-intensive audio and high-definition video footage of occurrences of animal group behavior of interest, such as predation. This project comprises three concurrent and interdependent research themes. The first is the investigation of methods to design and analyze the performance of distributed algorithms that implement autonomous decisions at the mobile agents, subject to communication and computational constraints. The second will pursue data-driven fundamental research on the modeling of animal group motion and will promote a formal understanding of the mechanisms of social interaction. The third is centered on the investigation of methods for hardware integration to build distributed networks of embedded devices that are capable of executing the newly developed algorithms, subject to power and weight constraints. The results and experience gained in this project will guide the development of effective autonomous systems for the monitoring and protection of endangered species. This project will create undergraduate and graduate research opportunities at all participating institutions, expanding on an existing collaboration between the University of Maryland, Princeton University, and the National Geographic Society. There is the potential for using wide-reaching media resources to disseminate the results of this project to a broad audience. This may contribute to attracting more students to engineering and science.

This project will construct a wireless network of animal-borne embedded devices that will be deployed and tested in a biologically-relevant application. The networked devices will provide not only geo-location data, but also execute cooperative strategies that save battery-life by selectively recording bandwidth-intensive audio and high-definition video footage of occurrences of animal group behavior of interest, such as predation. This project comprises three concurrent and interdependent research themes. The first is the investigation of methods to design and analyze the performance of distributed algorithms that implement autonomous decisions at the mobile agents, subject to communication and computational constraints. The second will pursue data-driven fundamental research on the modeling of animal group motion and will promote a formal understanding of the mechanisms of social interaction. The third is centered on the investigation of methods for hardware integration to build distributed networks of embedded devices that are capable of executing the newly developed algorithms, subject to power and weight constraints. The results and experience gained in this project will guide the development of effective autonomous systems for the monitoring and protection of endangered species. This project will create undergraduate and graduate research opportunities at all participating institutions, expanding on an existing collaboration between the University of Maryland, Princeton University, and the National Geographic Society. There is the potential for using wide-reaching media resources to disseminate the results of this project to a broad audience. This may contribute to attracting more students to engineering and science.

This project will construct a wireless network of animal-borne embedded devices that will be deployed and tested in a biologically-relevant application. The networked devices will provide not only geo-location data, but also execute cooperative strategies that save battery-life by selectively recording bandwidth-intensive audio and high-definition video footage of occurrences of animal group behavior of interest, such as predation. This project comprises three concurrent and interdependent research themes. The first is the investigation of methods to design and analyze the performance of distributed algorithms that implement autonomous decisions at the mobile agents, subject to communication and computational constraints. The second will pursue data-driven fundamental research on the modeling of animal group motion and will promote a formal understanding of the mechanisms of social interaction. The third is centered on the investigation of methods for hardware integration to build distributed networks of embedded devices that are capable of executing the newly developed algorithms, subject to power and weight constraints. The results and experience gained in this project will guide the development of effective autonomous systems for the monitoring and protection of endangered species. This project will create undergraduate and graduate research opportunities at all participating institutions, expanding on an existing collaboration between the University of Maryland, Princeton University, and the National Geographic Society. There is the potential for using wide-reaching media resources to disseminate the results of this project to a broad audience. This may contribute to attracting more students to engineering and science.

The objective of this inter-disciplinary research is to develop new technologies to transform the streets of a city into a hybrid transportation/communication system, called the Intelligent Road (iRoad), where autonomous wireless devices are co-located with traffic signals to form a wireless network that fuses real-time transportation data from all over the city to make a wide range of new applications possible. The approach is to build new capacities of quantitative bandwidth distribution, rate/delay assurance, and location-dependent security on a pervasive wireless platform through distributed queue management, adaptive rate control, and multi-layered trust. These new capacities lead to transformative changes in the way the transportation monitoring and control functions are designed and operated. Many technical challenges faced by the iRoad system are open problems. New theories/protocols developed in this project will support sophisticated bandwidth management, quality of service, multi-layered trust, and information fusion in a demanding environment where critical transportation functions are implemented. Solving these fundamental problems advances the state of the art in both wireless technologies and transportation engineering. The research outcome is likely to be broadly applicable in other wireless systems. The economic and societal impact of the iRoad system is tremendous at a time when the country is modernizing its ailing transportation infrastructure. It provides a pervasive communication infrastructure and engineering framework to build new applications such as real-time traffic map, online best-route query, intelligent fuel-efficient vehicles, etc. The research results will be disseminated through course materials, academic publication, industry connection, and presentations at the local transportation department.