Abstract
Harnessing wind energy is one of the pressing challenges of our time. The scale, complexity, and robustness of wind power systems present compelling cyber-physical system design issues. Leveraging the physical infrastructure at Purdue, this project aims to develop comprehensive computational infrastructure for distributed real-time control. In contrast to traditional efforts that focus on programming-in-the-small, this project emphasizes programmability, robustness, longevity, and assurance of integrated wind farms. The design of the proposed computational infrastructure is motivated by, and validated on, complex cyber-physical interactions underlying Wind Power Engineering. There are currently no high-level tools for expressing coordinated behavior of wind farms. Using the proposed cyber-physical system, the project aims to validate the thesis that integrated control techniques can significantly improve performance, reduce downtime, improve predictability of maintenance, and enhance safety in operational environments.
The project has significant broader impact. Wind energy in the US is the fastest growing source of clean, renewable domestically produced energy. Improvements in productivity and longevity of this clean energy source, even by a few percentage points will have significant impact on the overall energy landscape and decision-making. Mitigating failures and enhancing safety will go a long way towards shaping popular perceptions of wind farms -- accelerating broader acceptance within local communities. Given the relative infancy of "smart" wind farms, the potential of the project cannot be overstated.
Performance Period: 09/01/2011 - 04/30/2015
Institution: Purdue University
Sponsor: National Science Foundation
Award Number: 1136045
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Abstract
The computing landscape is a richly-heterogeneous space including both fixed and mobile nodes with a large variety of sensing, actuation and computational capabilities (including mobile devices, home electronics, taxis, robotic drones, etc.). Cyber-physical applications built on these devices have the potential to gather data on, analyze, and adapt to or control a range of environments. The challenge, however, is that Cyber-Physical Systems (CPSs) are difficult to program, and even more difficult to incorporate from one deployment to another, or to dynamically manage as nodes availability changes. Thus, CPS applications are too often programmed in a brittle fashion that impedes their ability to efficiently use available compute/sense/actuate resources beyond a one-shot deployment. In response, this project is improving CPS design and control in four primary thrusts. First, the project is developing CPSISA, an abstraction layer or intermediate representation to facilitate CPS applications expressing their compute/sense/actuate requirements to lower-level mapping and management layers. Second, the project is exploring methods of providing a Device Attribute Catalog (DAC) that summarizes a region?s available CPS nodes and their capabilities. Third, this research is improving and exploiting the ability to model, predict, and control the mobility of CPS nodes. When some CPS nodes are mobile, the accuracy and performance of a CPS application fundamentally is a function of where nodes will be positioned at any moment in time. This work exploits both static statistical coverage analysis and dynamic prediction and interpolation. Fourth, using CPSISA, DAC, and other resources as input, the team is developing tools to statically or dynamically optimize mappings of CPS applications onto available resources. To test ideas in a detailed and concrete manner, two applications are being studied and deployed. First, the FireGuide application for emergency response assistance uses groups of mobile/robotic nodes for guiding first responders in building fires. Second, a Regional Traffic Management (RTM) application demonstrates ideas at the regional level and will explore CPS scenarios for automobile traffic sensing and dynamic toll pricing.
The proposed research program has the potential for broad societal impact. Studies that improve how building emergencies are handled will improve emergency response safety both for occupants and for first responders around the country. Likewise, the deployment plans regarding regional traffic management will improve traffic patterns, fuel efficiency and quality-of-life for commuters across the United States. The research team is distributing the CPSISA, CPSMap, and CPSDyn software frameworks to allow other researchers and developers to make use of them. Extensive industry collaborations foster effective technology transfer. Finally, the project continues and broadens the PIs? prior track records for undergraduate research advising and for mentoring women students and members of under-represented minority groups.
Performance Period: 09/01/2011 - 08/31/2015
Institution: Carnegie Mellon University
Sponsor: National Science Foundation
Award Number: 1135874
Abstract
The national transmission networks that deliver high voltage electric power underpin our society and are central to the ongoing transformation of the American energy infrastructure. Transmission networks are very large and complicated engineering systems, and "keeping the lights on" as the transformation of the American energy infrastructure proceeds is a fundamental engineering challenge involving both the physical aspects of the equipment and the cyber aspects of the controls, communications, and computers that run the system. The project develops new principles of cyber-physical engineering by focusing on instabilities of electric power networks that can cause blackouts. It proposes novel approaches to analyze these instabilities and to design cyber-physical control methods to monitor, detect, and mitigate them. The controls must perform robustly in the presence of variability and uncertainty in electric generation, loads, communications, and equipment status, and during abnormal states caused by natural faults or malicious attacks.
The research produces cyber-physical engineering methodologies that specifically help to mitigate power system blackouts and more generally show the way forward in designing robust cyber-physical systems in environments characterized by rich dynamics and uncertainty. Education and outreach efforts involve students at high school, undergraduate, and graduate levels, as well as dissemination of results to the public and the engineering and applied science communities in industry, government and universities.
Performance Period: 09/01/2011 - 08/31/2015
Institution: University of California at Santa Barbara
Sponsor: National Science Foundation
Award Number: 1135819
Abstract
This project proposes a cross-layer framework in which vehicular wireless networking and platoon control interact with each other to tame cyber-physical uncertainties. Based on the real-time capacity region of wireless networking and the physical process of vehicle movements, platoon control selects its control strategies and the corresponding requirements on the timeliness and throughput of wireless data delivery to optimize control performance. Based on the requirements from platoon control, wireless networking controls co-channel interference and adapts to cyber-physical uncertainties to ensure the timeliness and throughput of single-hop as well as multi-hop broadcast. For proactively addressing the impact of vehicle mobility on wireless broadcast, wireless networking also leverages input from platoon control on vehicle movement prediction. In realizing the cross-layer framework, wireless scheduling ensures agile, predictable interference control in the presence of cyber-physical uncertainties.
If successful, this project will lead to a general and rigorous framework for wireless vehicular cyber-physical network control that will enable safe, efficient, and clean transportation. The principles and techniques for taming cyber-physical uncertainties will provide insight into other application domains of wireless networked sensing and control such as unmanned aerial vehicles and smart power grids. This project will also develop integrative research and education on wireless cyber-physical systems through multi-level, multi-component educational activities.
Hongwei Zhang
I lead the Dependable Networking and Computing research group in the Department of Electrical and Computer Engineering at Iowa State University. Our research explores the theories, methods, and systems building-blocks for addressing dynamics and uncertainties in networked systems that involve wireless networks, sensing and control networks, vehicular networks, and the Internet.
Presently, we are especially interested in the modeling, algorithmic, and systems issues in wireless sensing and control networks as well as their applications in augmented reality, smart agriculture, connected and automated vehicles, smart energy grid, industrial IoT, and cyber-physical-human systems in general. For instance, as a part of the US Ignite, CPS, NeTS, and GOALI programs of NSF and in collaboration with industry, we have investigated field-deployable mechanisms for predictable, real-time, and secure wireless networking, and we have investigated cross-layer approaches to taming cyber-physical uncertainties in wireless networked sensing and control; as a part of the NSF GENI program, we have developed theoretical and systems foundations for experimentation and service provisioning in connected and automated vehicles as well as in federated, networked sensing.
Besides publications, our work has provided foundational components for several wireless network systems, including the emulation system and software-defined innovation platforms for sensing and control networks of connected and automated vehicles, the WiMAX research cellular network, the KanseiGenie federated sensor networks, the NetEyeexperimental infrastructure (which has 176 IEEE 802.15.4 nodes and 15 802.11b/g nodes), and the DARPA sensor network systems A Line in the Sand and ExScal (which, with its 200-node 802.11b mesh network and 1,200-node mote network, was the world's largest wireless sensor network and 802.11b mesh network deployed at its time).
I received the NSF CAREER Award in 2011. Our work has been recognized by the Best Demo Award at the 23rd and 21st NSF GENI Engineering Conference in 2015 and 2014 respectively, and the Best Demo First Runner-up Award at the 20th NSF GENI Engineering Conference in 2014. Our articles have been selected as the Annual Best Paper of the Journal of Systems Science and Complexity (Springer) in 2016, the Spotlight Paper of the IEEE Transactions on Mobile Computing in November 2010, and a Best Paper Candidate at the IEEE International Conference on Network Protocols (ICNP) in 2010. Our work has also been featured by public media such as CBS, Science X, SmartPlanet, EurekAlert!, Model D, UMTRI Connected Vehicle News, Michigan University Research Corridor News, Wayne State University New Science Magazine, and Today@Wayne.
Performance Period: 09/01/2011 - 08/31/2015
Institution: Wayne State University
Sponsor: National Science Foundation
Award Number: 1136007
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Abstract
The focus of this project is the efficient implementation of multiple control and non-control automotive applications in a distributed embedded system (DES) with a goal of developing safe, dependable, and secure Automotive CPS. DES are highly attractive due to the fact that they radically enhance the capabilities of the underlying system by linking a range of devices and sensors and allowing information to be processed in unprecedented ways. Deploying control and non-control applications on a modern DES, which uses advanced processor and communication technology, introduces a host of challenges in their analysis and synthesis, and leads to a large semantic gap between models and their implementation. This gap will be filled via the development of a novel CPS architecture by stitching together common fundamental principles of multimodality from real-time systems and related notions of switching in control theory and integrating them into a co-design of real-time platforms and adaptive controllers. This architecture will be validated at the Toyota Technical Center in the context of engine control and diagnostics.
The results of this project will provide the science and technology for a foundation in any and all infrastructure systems ranging from finance and energy to telecommunication and transportation where distributed embedded systems are present. In addition to training the graduate and undergraduate students, and mentoring a post-doctoral associate who will gain multi-domain expertise in advanced control, real-time computation and communication, and performance analysis, an inter-school graduate and an integrated summer course will be developed on control in embedded systems and combined with on-going outreach programs at MIT and UPenn for minority and women undergraduate students and K-12 students.
Anuradha Annaswamy
Dr. Anuradha Annaswamy received the Ph.D. degree in Electrical Engineering from Yale University in 1985. She has been a member of the faculty at Yale, Boston University, and MIT where currently she is the director of the Active-Adaptive Control Laboratory and a Senior Research Scientist in the Department of Mechanical Engineering. Her research interests pertain to adaptive control theory and applications to aerospace and automotive control, active control of noise in thermo-fluid systems, control of autonomous systems, decision and control in smart grids, and co-design of control and distributed embedded systems. She is the co-editor of the IEEE CSS report on Impact of Control Technology: Overview, Success Stories, and Research Challenges, 2011, and will serve as the Editor-in-Chief of the IEEE Vision document on Smart Grid and the role of Control Systems to be published in 2013. Dr. Annaswamy has received several awards including the George Axelby Outstanding Paper award from the IEEE Control Systems Society, the Presidential Young Investigator award from the National Science Foundation, the Hans Fisher Senior Fellowship from the Institute for Advanced Study at the Technische Universität München in 2008, and the Donald Groen Julius Prize for 2008 from the Institute of Mechanical Engineers. Dr. Annaswamy is a Fellow of the IEEE and a member of AIAA.
Performance Period: 10/01/2011 - 09/30/2016
Institution: Massachusetts Institute of Technology
Sponsor: National Science Foundation
Award Number: 1135815
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Abstract
In many important situations, analytically predicting the behavior of physical systems is not possible. For example, the three dimensional nature of physical systems makes it provably impossible to express closed-form analytical solutions for even the simplest systems. This has made experimentation the primary modality for designing new cyber-ph0.00000..0000... 0ysical systems (CPS). Since physical prototyping and experiments are typically costly and hard to conduct, "virtual experiments" in the form of modeling and simulation can dramatically accelerate innovation in CPS. Unfortunately, major technical challenges often impede the effectiveness of modeling and simulation. This project develops foundations and tools for overcoming these challenges. The project focuses on robotics as an important, archetypical class of CPS, and consists of four key tasks: 1) Compiling and analyzing a benchmark suite for modeling and simulating robots, 2) Developing a meta-theory for relating cyber-physical models, as well as tools and a test bed for robot modeling and simulation, 3) Validating the research results of the project using two state-of-the-art robot platforms that incorporate novel control technologies and will require novel programming techniques to fully realize their potential 4) Developing course materials incorporating the project's research results and test bed. With the aim of accelerating innovation in a wide range of domains including stroke rehabilitation and prosthetic limbs, the project is developing new control concepts and modeling and simulation technologies for robotics. In addition to new mathematical foundations, models, and validation methods, the project will also develop software tools and systematic methods for using them. The project trains four doctoral students; develops a new course on modeling and simulation for cyber-physical systems that balances both control and programming concepts; and includes an outreach component to the public and to minority-serving K-12 programs.
Performance Period: 09/15/2011 - 11/30/2015
Institution: Texas Engineering Experiment Station
Sponsor: National Science Foundation
Award Number: 1136104
Abstract
Effective response and adaptation to the physical world, and rigorous management of such behaviors through programmable computational means, are mandatory features of cyber physical systems (CPS). However, achieving such capabilities across diverse application requirements surpasses the current state of the art in system platforms and tools. Current computational platforms and tools often treat physical properties individually and in isolation from other cyber and physical attributes. They do not adequately support the expression, integration, and enforcement of system properties that span cyber and physical domains. This results in inefficient use of both cyber and physical resources, and in lower system effectiveness overall.
This work investigates novel approaches to these important problems, based on modularizing and integrating diverse cyber-physical concerns that cross-cut physical, hardware, instruction set, kernel, library, and application abstractions. The three major thrusts of this research are 1) establishing foundational models for expressing, analyzing, enforcing, and measuring different conjoined cyber-physical properties, 2) conducting a fundamental re-examination of system development tools and platforms to identify how different application concerns that cut across them can be modularized as cyber-physical system aspects, and 3) developing prototype demonstrations of our results to evaluate further those advances in the state of the art in aspect-oriented techniques for CPS, to help assess the feasibility of broader application of the approach. The broader impact of this work will be through dissemination of academic papers, and open platforms and tools that afford unprecedented integration of cyber-physical properties.
Performance Period: 10/01/2010 - 12/31/2014
Sponsor: Iowa State University
Award Number: 1060337
Abstract
The objective of this research is to develop methods for the operation and design of cyber physical systems in general, and energy efficient buildings in particular. The approach is to use an integrated framework: create models of complex systems from data; then design the associated sensing-communication-computation-control system; and finally create distributed estimation and control algorithms, along with execution platforms to implement these algorithms. A special emphasis is placed on adaptation. In particular, buildings and their environments change with time, as does the way in which buildings are used. The system must be designed to detect and respond to such changes.
The proposed research brings together ideas from control theory, dynamical systems, stochastic processes, and embedded systems to address design and operation of complex cyber physical systems that were previously thought to be intractable. These approaches provide qualitative understanding of system behavior, algorithms for control, and their implementation in a networked execution platform. Insights gained by the application of model reduction and adaptation techniques will lead to significant developments in the underlying theory of modeling and control of complex systems.
The research is expected to directly impact US industry through the development of integrated software-hardware solutions for smart buildings. Collaborations with United Technologies Research Center are planned to enhance this impact. The techniques developed are expected to apply to other complex cyber-physical systems with uncertain dynamics, such as the electric power grid. The project will enhance engineering education through the introduction of cross-disciplinary courses.
Luca Carloni
Luca Carloni is an Associate Professor of Computer Science at Columbia University in the City of New York, where he leads the System-Level Design Group. He holds a Laurea Degree Summa cum Laude in Electronics Engineering from the University of Bologna, Italy, a Master of Science in Engineering from the University of California at Berkeley, and a Ph.D. in Electrical Engineering and Computer Sciences from the University of California at Berkeley.
At Berkeley Luca was the 2002 recipient of the Demetri Angelakos Memorial Achievement Award in recognition of altruistic attitude towards fellow graduate students. Luca received the Faculty Early Career Development (CAREER) Award from the National Science Foundation in 2006, was selected as an Alfred P. Sloan Research Fellow in 2008, received the ONR Young Investigator Award in 2010 and the IEEE CEDA Early Career Award in 2012.
His research interests include methodologies and tools for multi-core system-on-chip platforms with emphasis on system-level design and communication synthesis, design and optimization of networks-on-chip, embedded software and distributed embedded systems. Luca coauthored over ninety refereed papers and is the holder of one patent.
Luca is an associate editor of the ACM Transactions in Embedded Computing Systems and the Elsevier Journal of Sustainable Computing. He has served in the technical program committee of several conferences including DAC, DATE, ICCAD, and EMSOFT. In 2010 he served as technical program co-chair of the International Conference on Embedded Software (EMSOFT), the International Symposium on Networks-on-Chip (NOCS), and the International Conference on Formal Methods and Models for Codesign (MEMOCODE).
In 2013 Luca serves as general chair of Embedded Systems Week (ESWeek), the premier event covering all aspects of embedded systems and software.
Luca participates in the Gigascale Systems Research Center (GSRC).
Performance Period: 03/01/2010 - 02/28/2015
Institution: Columbia University
Sponsor: National Science Foundation
Award Number: 0931870
Abstract
The objective of this research is to develop a cyber-physical system composed of accelerometers and novel machine learning algorithms to analyze data in the context of a set of driving health care applications. The approach is to develop novel machine learning algorithms for temporal segmentation, classification, and detection of subtle elements of human motion. These techniques will allow quantification of human motion and improved full-time monitoring and assessment of medical conditions using a lightweight wearable system. The scientific contribution of this research is in advancing machine learning and human sensing in support of improved medical diagnoses and treatment monitoring by (i) modeling human activity and symptoms through sensor data analysis, (ii) integrating and fusing information from several accelerometers to monitor in real-time, (iii) validating the efficacy of the automated detection through assessments applying the state of the art in diagnostic evaluation, (iv) developing novel machine learning methods for temporal segmentation, classification, and discovery of multiple temporal patterns that discriminate between temporal signals, and (v) providing quality measures to characterize subtle human motion. These algorithms will advance machine learning in the area of unsupervised and semisupervised learning. The driving applications for this research are job coaching for people with cognitive disabilities, tele-rehabilitation for knee osteo-arthritis, assessing variability in balance and gait as an indicator of health of older adults, and measures for assessing Parkinson's patients. This research is highly interdisciplinary and will train graduate students for careers in developing technological innovations in health and monitoring systems.
Performance Period: 09/01/2009 - 08/31/2012
Institution: University of Pittsburgh
Sponsor: National Science Foundation
Award Number: 0931595