CPS: Breakthrough: A Dynamic Optimization Framework for Connected Automated Vehicles in Urban Environments
Lead PI:
Christos Cassandras
Abstract
Connected Automated Vehicles (CAVs), often referred to as "self-driving cars", will have a profound impact not only on transportation systems, but also in terms of associated economic, environmental, and social effects. As with any such major transformative undertaking, quantifying the magnitude of its expected impact is essential. The first part of this project aims at precisely this quantification (also referred to as the "price of anarchy") by assessing the difference between the performance of a transportation system as it now stands and the performance achievable in a CAV-based environment. A well-designed CAV-based transportation network has the benefit of expanding limited roadway capacity without affecting the existing infrastructure, but rather by seeking novel ways which focus on the vehicles and not the roads. A major part of the proposed project will focus on meeting this goal at the weakest links of a transportation system: the bottleneck points defined by intersections and merging points. The project will use inverse optimization techniques applied to large traffic datasets (from the Eastern Massachusetts road network) to infer unobservable factors, such as user behavior, and use them to construct a predictive model of traffic equilibria. Based on these new traffic demand models, forward optimization problems will be solved which will lead to socially optimal traffic flow equilibria achievable through a CAV-based system. A dynamic optimization framework will also be developed for urban intersections where the motion of CAVs will be controlled based on real-time data communicated over a wireless network to operate both safely and efficiently in a highly dynamic and uncertain environment. Towards this goal, the broader technical challenge of solving dynamic optimization problems on line will be addressed through novel ways that exploit event-driven methodologies with wide applicability in Cyber-Physical Systems. The overall framework will be demonstrated by implementing the key concepts and explicit control and optimization mechanisms in a miniature city test bed with an urban landscape and small mobile robots emulating CAVs with the ability to communicate and share data
Christos Cassandras

Christos G. Cassandras is Head of the Division of Systems Engineering and Professor of Electrical and Computer Engineering at Boston University. He is also co-founder of Boston University’s Center for Information and Systems Engineering (CISE). He received degrees from Yale University (B.S., 1977), Stanford University (M.S.E.E., 1978), and Harvard University (S.M., 1979; Ph.D., 1982). In 1982-84 he was with ITP Boston, Inc. where he worked on the design of automated manufacturing systems. In 1984-1996 he was a faculty member at the Department of Electrical and Computer Engineering, University of Massachusetts/Amherst. He specializes in the areas of discrete event and hybrid systems, stochastic optimization, and computer simulation, with applications to computer and sensor networks, manufacturing systems, and transportation systems. He has published over 300 refereed papers in these areas, and five books. He has guest-edited several technical journal issues and serves on several journal Editorial Boards. He has recently collaborated with The MathWorks, Inc. in the development of the discrete event and hybrid system simulator SimEvents.

      Dr. Cassandras was Editor-in-Chief of the IEEE Transactions on Automatic Control from 1998 through 2009 and has also served as Editor for Technical Notes and Correspondence and Associate Editor. He is the 2012 President of the IEEE Control Systems Society (CSS) and has served as Vice President for Publications and on the Board of Governors of the CSS. He has chaired the CSS Technical Committee on Control Theory, and served as Chair of several conferences. He has been a plenary speaker at many international conferences, including the American Control Conference in 2001 and the IEEE Conference on Decision and Control in 2002, and an IEEE Distinguished Lecturer.

      He is the recipient of several awards, including the 2011 IEEE Control Systems Technology Award, the Distinguished Member Award of the IEEE Control Systems Society (2006), the 1999 Harold Chestnut Prize (IFAC Best Control Engineering Textbook) for Discrete Event Systems: Modeling and Performance Analysis, a 2011 prize for the IBM/IEEE Smarter Planet Challenge competition, a 1991 Lilly Fellowship and a 2012 Kern Fellowship. He is a member of Phi Beta Kappa and Tau Beta Pi. He is also a Fellow of the IEEE and a Fellow of the IFAC.

Performance Period: 04/01/2017 - 03/31/2020
Institution: Trustees of Boston University
Sponsor: National Science Foundation
Award Number: 1645681
CPS: Synergy: Collaborative Research: Mapping and Querying Underground Infrastructure Systems
Lead PI:
Roberto Tamassia
Abstract
One of the challenges toward achieving the vision of smart cities is improving the state of the underground infrastructure. For example, large US cities have thousands of miles of aging water mains, resulting in hundreds of breaks every year, and a large percentage of water consumption that is unaccounted for. The goal of this project is to develop models and methods to generate, analyze, and share data on underground infrastructure systems, such as water, gas, electricity , and sewer networks. The interdisciplinary team of investigators from the University of Illinois at Chicago, Brown University, and Northwestern University will leverage partnerships with the cities of Chicago and Evanston, Illinois, to make the approach and findings relevant to their stakeholders. Research results will be incorporated in courses at the three institutions. Outreach efforts include events for K-12 students to develop awareness about underground infrastructure from a data and computational perspective. The results of the project will ultimately help municipalities maintain and renovate civil infrastructure in a more effective manner. Cities are cyber-physical systems on a grand scale, and developing a precise knowledge of their infrastructure is critical to building a foundation for the future smart city. This proposal takes an information centric approach based on the complex interaction among thematic data layers to developing, visualizing, querying, analyzing, and providing access to a comprehensive representation of the urban underground infrastructure starting from incomplete and imprecise data. Specifically, the project has the following main technical components: (1) Generation of accurate GIS-based representations of underground infrastructure systems from paper maps, CAD drawings, and other legacy data sources; (2) Visualization of multi-layer networks combining schematic overview diagrams with detailed geometric representations; (3) Query processing algorithms for integrating spatial, temporal, and network data about underground infrastructure systems; (4) Data analytics spanning heterogeneous geospatial data sources and incorporating uncertainty and constraints; (5) Selective access to stakeholders on a need-to-know basis and facilitating data sharing; and (6) Evaluation in collaboration with the cities of Chicago and Evanston.
Performance Period: 09/01/2016 - 08/31/2019
Institution: Brown University
Sponsor: National Science Foundation
Award Number: 1645661
CPS: Frontier: Collaborative Research: Data-Driven Cyberphysical Systems
Lead PI:
Sandipan Mishra
Abstract
Data-driven cyber-physical systems are ubiquitous in many sectors including manufacturing, automotive, transportation, utilities and health care. This project develops the theory, methods and tools necessary to answer the central question "how can we, in a data-rich world, design and operate cyber-physical systems differently?" The resulting data-driven techniques will transform the design and operation process into one in which data and models - and human designers and operators - continuously and fluently interact. This integrated view promises capabilities beyond its parts. Explicitly integrating data will lead to more efficient decision-making and help reduce the gap from model-based design to system deployment. Furthermore, it will blend design- and run-time tasks, and help develop cyber-physical systems not only for their initial deployment but also for their lifetime. While proposed theory, methods and tools will cut across the spectrum of cyber-physical systems, the project focuses on their implications in the emerging application of additive manufacturing. Even though a substantial amount of engineering time is spent, additive manufacturing processes often fail to produce acceptable geometric, material or electro-mechanical properties. Currently, there is no mechanism for predicting and correcting these systematic, repetitive errors nor to adapt the design process to encompass the peculiarities of this manufacturing style. A data-driven cyber-physical systems perspective has the potential to overcome these challenges in additive manufacturing. The project's education plan focuses on the already much needed transformation of the undergraduate and graduate curricula to train engineers and computer scientists who will create the next-generation of cyber-physical with a data-driven mindset. The team will reach out to K-12 students and educators through a range of activities, and to undergraduate students from underrepresented groups through year-long research projects. All educational material generated by the project will be shared publicly.
Performance Period: 10/01/2017 - 09/30/2020
Institution: Rensselaer Polytechnic Institute
Sponsor: National Science Foundation
Award Number: 1645648
CPS: Breakthrough: Collaborative Research: Transactive Control of Smart Railway Grid
Lead PI:
Sudip Mazumder
Abstract
This project pursues a smart cyber-physical approach for improving the electric rail infrastructure in the United States and other nations. We will develop a distributed coordination of pricing and energy utilization even while ensuring end-to-end time schedule constraints for the overall rail infrastructure. We will ensure this distributed coordination through transactive control, a judicious design of dynamic pricing in a cyber-physical system that utilizes the computational and communication infrastructure and accommodates the physical constraints of the underlying train service. The project is synergistic in that it builds upon the expertise of the electric-train infrastructure and coordination at UIC and that of transactive control on the part of MIT. We will validate the approach through collaboration with engineers in the Southeastern Pennsylvania Transport Authority, where significant modernization efforts are underway to improve their electric-train system. The project also involves strong international collaboration which will also enable validation of the technologies. This project will formulate a multi-scale transitive control strategy for minimization of price and energy utilization in a geographically-dispersed railway grid with broader implications for evolving smart and micro grids. The transactions evolve over different temporal scales ranging from day-ahead offline transaction between the power grid and the railway system operators yielding price optimality to real-time optimal transaction among the trains or the area control centers (ACC). All of these transactions are carried out while meeting system constraints ranging from end-to-end time-scheduling, power-quality, and capacity. Our research focuses on fundamental issues encompassing integration of information, control, and power, including event-driven packet arrival from source to destination nodes while ensuring hard relative deadlines and optimal sampling and sensing; and formulation of network concave utility function for allocating finite communication-network capacity among control loops. The project develops optimization approaches that can be similarly applied across multiple application domains.
Sudip Mazumder

Sudip K. Mazumder is the Director of Laboratory for Energy and Switching-Electronics Systems and a Professor in the Department of Electrical and Computer Engineering at UIC. He has over 22 years of professional experience and has held R&D and design positions in leading industrial organizations and has served as Technical Consultant for several industries. Dr. Mazumder also serves as the President of NextWatt LLC, a small business organization that he setup in 2008. His current areas of interests are a) Interactive power-electronics/power networks, smart grid, and energy storage; b) Renewable and alternative energy based power electronics systems for distributed generation and microgrid; and c) Optically-triggered wide-bandgap power-electronics device and control technologies and SiC and GaN device based high-frequency, high-temperature, and high-voltage power electronics. Since joining UIC in 2001, Dr. Mazumder has been awarded about 40 sponsored projects by NSF, DOE, ONR, ARPA-E, CEC, EPA, AFRL, NASA, NAVSEA, and multiple leading industries in above-referenced areas. He has published over 150 refereed papers in prestigious journals and conferences and has published 1 book and 6 book chapters. About 50% of his journal papers are published in IEEE transactions with a current impact factor close to 5. Dr. Mazumder has presented 47 invited/plenary/keynote presentations and currently, he also holds 7 issued and 3 pending patents.

Dr. Mazumder received his Ph.D. degree from the Department of Electrical and Computer Engineering of the Virginia Polytechnic and State University (VPI&SU - also known as Virginia Tech) in 2001. He received his M.S. degree from the Department of Electrical Power Engineering of the Rensselaer Polytechnic Institute (RPI) in 1993. He received his B.E. degree from the Department of Electrical Engineering of University of Delhi, India in 1989 with distinction.

Dr. Mazumder received in 2013, the prestigious University Scholar Award from the University of Illinois and in 2011, the Teaching Recognition Program (TRP) Award at UIC. In 2008 and 2006, he received the prestigious Faculty Research Award from UIC for outstanding research performance and excellent scholarly activities. He also received the ONR Young Investigator Award and NSF CAREER Awards in 2005 and 2003, respectively, and prestigious IEEE Prize Paper Awards in 2002, 2007, and 2013 respectively. He also received the best paper presentation in a session award certificate from IEEE Industrial Electronics Conference in 2004 and 2012. In 2005, he led a team of University of Illinois, Chicago student team to first place in USA and third place in the world as a part of the highly reputed IEEE sponsored International Future Energy Challenge competition.

Dr. Mazumder served as the first Editor-in-Chief for International Journal of Power Management Electronics (currently known as Advances in Power Electronics) between 2006 and 2009. Currently, he also serves as the Guest-Editor-in-Chief for IEEE Transactions on Power Electronics Special Issue on High-Frequency-Link Power-Conversion Systems (2013-2014) and the lead Guest Editor for IEEE Transactions on Industrial Electronics Special Section on Control Strategies for Spatially Distributed Interactive Power Networks (2013-2014).

Currently, Dr. Mazumder also serves as an Associate Editor for EEE Transactions on Power Electronics (since 2009), IEEE Transactions on Industrial Electronics (since 2003), and IEEE Transactions on Aerospace and Electronics Systems (since 2008). He is also an Editorial Board Member for Advances in Power Electronics. Previously, he has also served as an Associate Editor for IEEE Transactions on Circuits and Systems and IEEE Power Electronics Letter. He has also served as the Guest Co-Editor for the following Transaction Special Issues: IEEE Transactions on Power Electronics Special Issue on Power Electronics in DC Distribution Systems (2011-2013) and Advances in Power Electronics Special Issue on Advances in Power Electronics for Renewable Energy (2010-2011).

In 2010, Dr. Mazumder served as the Chair, Student/Industry Coordination Activities for IEEE Energy Conversion Congress and Exposition, which is the largest conference in power electronics today in North America. He served as the Co-Chair for of IEEE Power Electronics Society (PELS) Technical Committee on Sustainable Energy Systems (SES) and currently serving as the Technical Awards Committee Chair for SES. Currently, he is also serving as the Vice Chair of IEEE PELS Subcommittee on Distributed Generation and Renewable Energy. He is also serving as the Advisory Committee Member for 2012 IEEE India International Conference on Power Electronics and has also served in the same capacity for 2010 IEEE International Symposium on Power Electronics for Distributed Generation Systems. He is serving/has served as Technical Program Committee Member for numerous IEEE sponsored and other reputed conferences including IEEE Energy Conversion Congress and Exposition, IEEE Applied Power Electronics Conference and Exposition, IEEE Industrial Electronics Conference, IEEE International Symposium on Power Electronics for Distributed Generation Systems.

Dr. Mazumder has been invited on by the inaugural 2012 Clean Energy Trust Show Case, an event that will connect entrepreneurs, investors and researchers who can work together to commercialize the latest clean technology, to deliver his vision on Smart Grid. Between 2010 and 2011, he also served as an Advisory Council Member for Vice Chancellor for Research's Urban Resilience and Global Environment at UIC. In 2009 and 2010, he also served as the Expert Representative on Smart Grid for UIC at the Midwestern Great Lakes Alliance for Sustainable Energy Research (GLASER) initiative. In 2008, he was invited by DOE to participate along with several leading industries and selected academic professionals regarding High MW Power Converter for next generation power grid. In 2008, he was invited by NSF to participate in a unique workshop (comprising leading industries and research experts) leading to decision on nation's specific R&D focus on energy and energy distribution over the next ten and fifty years. Dr. Mazumder has also been invited to serve as the Working Group Committee Member for IEEE P1676, which focuses on Control Architecture for High Power Electronics (> 1 MW) used in Electric Power Transmission and Distribution Systems. In 2009, Dr. Mazumder was also part of the team that wrote the National Science Foundation and National Coordination Office for Networking and Information Report on Research Directions for Future Cyber-Physical Energy Systems. Dr. Mazumder has delivered over 43 invited/keynote/plenary lectures, presentations, and tutorials to leading conferences, national laboratories, universities, and industries and has served as a panel reviewer and reviewer for NSF, DOE, ARPA-E, CRDF, and AAAS.

Performance Period: 09/01/2017 - 08/31/2019
Institution: University of Illinois at Chicago
Sponsor: National Science Foundation
Award Number: 1644874
CPS: Synergy: Collaborative Research: In-Silico Functional Verification of Artificial Pancreas Control Algorithms
Lead PI:
Fraser Cameron
Abstract
Title: CPS:Synergy:Collaborative Research: In-Silico Functional Verification of Artificial Pancreas Control Algorithms. The project investigates a formal verification framework for artificial pancreas (AP) controllers that automate the delivery of insulin to patients with type-1 diabetes (T1D). AP controllers are safety critical: excessive insulin delivery can lead to serious, potentially fatal, consequences. The verification framework under development allows designers of AP controllers to check that their control algorithms will operate safely and reliably against large disturbances that include patient meals, physical activities, and sensor anomalies including noise, delays, and sensor attenuation. The intellectual merits of the project lie in the development of state-of-the-art formal verification tools, that reason over mathematical models of the closed-loop including external disturbances and insulin-glucose response. These tools perform an exhaustive exploration of the closed loop system behaviors, generating potentially adverse situations for the control algorithm under verification. In addition, automatic techniques are being investigated to help AP designers improve the control algorithm by tuning controller parameters to eliminate harmful behaviors and optimize performance. The broader significance and importance of the project are to minimize the manual testing effort for AP controllers, integrate formal tools in the certification process, and ultimately ensure the availability of safe and reliable devices to patients with type-1 diabetes. The framework is made available to researchers who are developing AP controllers to help them verify and iteratively improve their designs. The team is integrating the research into the educational mission by designing hands-on courses to train undergraduate students in the science of Cyber-Physical Systems (CPS) using the design of AP controllers as a motivating example. Furthermore, educational material that explains the basic ideas, current challenges and promises of the AP concept is being made available to a wide audience that includes patients with T1D, their families, interested students, and researchers. The research is being carried out collaboratively by teams of experts in formal verification for Cyber-Physical Systems, control system experts with experience designing AP controllers, mathematical modeling experts, and clinical experts who have clinically evaluated AP controllers. To enable the construction of the verification framework from the current state-of-the-art verification tools, the project is addressing major research challenges, including (a) building plausible mathematical models of disturbances from available clinical datasets characterizing human meals, activity patterns, and continuous glucose sensor anomalies. The resulting models are integrated in a formal verification framework; (b) simplifying existing models of insulin glucose response using smaller but more complex delay differential models; (c) automating the process of abstracting the controller implementation for the purposes of verification; (d) producing verification results that can be interpreted by control engineers and clinical researchers without necessarily understanding formal verification techniques; and (e) partially automating the process of design improvements to potentially eliminate severe faults and improve performance. The framework is evaluated on a set of promising AP controller designs that are currently under various stages of clinical evaluation.
Performance Period: 09/01/2016 - 09/30/2018
Institution: Rensselaer Polytechnic Institute
Sponsor: National Science Foundation
Award Number: 1641327
Collaborative Research: EAGER: Fusion of Data and Power for a Controllable Delivery Power Grid
Lead PI:
Roberto Rojas-Cessa
Abstract
Currently, electrical power distribution systems rely on permanently energized grids - electricity is transmitted constantly from the provider to users. Consequently, loads can be connected to the power grid without prior consent from the provider, giving rise to discretionary load access and thereby straining the grid's stability. Safety margins are required to satisfy sudden and spontaneous demands. At the same time, the intermittent availability of renewable sources adds yet another pressing condition on balancing existing grids. Finally, the discretionary and essentially unrestricted access to power comes at the price of grid vulnerabilities, such as cascading failures. The PIs propose a proactive digital management approach to the power grid called the controlled-delivery grid (CDG), which is cognizant of the load before energy is delivered. Users have specific addresses and issue requests for energy in advance and for a specific duration of time in the CDG. Distribution points then manage the amount and duration of energy delivery. The PIs central hypothesis is that techniques derived from network management and control research can be applied to the smart grid enabling more efficient, more robust, and more secure delivery of energy. This high-risk / high-reward proposal will investigate feasibility of this approach. Through the implementation and use of a micro-grid test bed, the PIs propose to analyze the feasibility of fusion of digital data and high-voltage signals and demonstrate seamless integration of alternative energy sources (e.g., solar energy) with the micro-grid while using the CDG's framework. The PIs will also study methods for allocation and distribution of electrical power for a CDG framework. The novel concept ascertains targeted delivery of energy to specific users, thus simultaneously minimizing overall power overhead and conservation of non-renewable energy resources. This new grid will also increase robustness against failures and enable sudden resumption of service through proactive surge management, an ability to negotiate reduced power rates in return for lower consumption in real time, and an ability to route energy from alternative sources back to the grid without compromising grid stability.
Performance Period: 08/15/2016 - 07/31/2019
Institution: New Jersey Institute of Technology
Sponsor: National Science Foundation
Award Number: 1641033
EAGER: Agile Data Integration to Facilitate Scaling of Air Quality Research
Lead PI:
Kristin Tufte
Abstract
Transportation, through vehicle emissions, has a significant impact on air pollution in urban areas - presenting health risks to pedestrians, vehicle occupants and transit users. Air pollutant concentrations near roadways may be up to orders of magnitude higher than average air pollutant levels in urban areas. Further, according to the EPA, transportation accounts for 26% of greenhouse gasses in the United States. Recent research at Portland State University, in collaboration with the City of Portland, Oregon, indicates that a relatively simple technique - modifying the timings of traffic signals - has the potential to reduce vehicle emissions in cities. However, this preliminary research needs to be explored more fully to evaluate its potential. This project would scale the work from a single location to a full transportation corridor, namely the Powell-Division Corridor. In addition, this project will investigate how data management technology can be applied to scale the air quality analysis. The techniques resulting from our exploratory research are expected to inform efforts to advance 'Smart City' approaches in multiple domains. The proposed project capitalizes on unique and time-sensitive resources and opportunities to design an innovative and potentially transformative approach to address a globally relevant problem - reducing traffic-related air emissions. The proposed work has the potential to affect the lives of urban citizens by identifying a relatively easy to implement method for reducing vehicle emissions and thereby reducing greenhouse gas emissions, improving air quality and reducing pedestrian exposure to air pollutants. This project directly contributes to the Portland (Oregon) Global Cities Challenge (GCTC) Action Cluster, recently awarded the $20,000 leadership award at the GCTC 2016 Exposition. The project also aligns with the City of Portland's Ubiquitous Mobility for Portland (UB Mobile PDX) initiative, one of seven finalists in the U.S. Department of Transportation Smart Cities Challenge. This project will investigate and develop Cyber Physical Systems technology, particularly data management technology, to address systematic issues observed in data cleaning and data integration of air quality and transportation data. In practice, data integration and cleaning are still typically automated in an ad-hoc fashion; existing systematic data integration and cleaning technologies do not effectively support scaling of these processes. This project proposes to develop a concept we call Agile Integration, which is designed to address the complex dynamics associated with rapid increases in environmental sensing data, the accelerating pace of change in cities, and mounting pressures on data-intensive decision making. Techniques for semi-automated data cleaning and processing will also be developed to better capture human decisions and judgments that go into data integration. The results will be implemented in a prototype Cyber-Physical System for Data Integration for dense sensor networks. In the long term, the proposed work has the potential to impact the lives of everyday citizens by validating a potential method for reducing vehicle emissions through signal timing changes. Vehicle emissions in urban areas impact greenhouse gas emissions and urban air quality. Since minority and low-socioeconomic status populations disproportionately reside in close proximity to major roadways, the potential impacts of this project directly affect those often underserved populations. Further, the scalability problems described above, while exemplified by the air quality research for this proposal, also appear in the transportation domain and in others such as healthcare, education and environmental sensing. Thus the techniques developed through this project are expected to be extensible to those domains. The work will produce an improved understanding of lower-cost air quality sensors. In terms of educational goals, this project will engage students from PSU?s atmospheric science REU that specifically recruits Native American and rural Oregonians. Results will be disseminated to the computer science, transportation, and air quality professional communities, thus impacting at least three research domains.
Performance Period: 09/01/2016 - 08/31/2018
Institution: Portland State University
Sponsor: National Science Foundation
Award Number: 1640749
EAGER: Collaborative Research: Fusion of Data and Power for a Controllable Delivery Power Grid
Lead PI:
Ahmed Mohamed
Abstract
Currently, electrical power distribution systems rely on permanently energized grids - electricity is transmitted constantly from the provider to users. Consequently, loads can be connected to the power grid without prior consent from the provider, giving rise to discretionary load access and thereby straining the grid's stability. Safety margins are required to satisfy sudden and spontaneous demands. At the same time, the intermittent availability of renewable sources adds yet another pressing condition on balancing existing grids. Finally, the discretionary and essentially unrestricted access to power comes at the price of grid vulnerabilities, such as cascading failures. The PIs propose a proactive digital management approach to the power grid called the controlled-delivery grid (CDG), which is cognizant of the load before energy is delivered. Users have specific addresses and issue requests for energy in advance and for a specific duration of time in the CDG. Distribution points then manage the amount and duration of energy delivery. The PIs central hypothesis is that techniques derived from network management and control research can be applied to the smart grid enabling more efficient, more robust, and more secure delivery of energy. This high-risk / high-reward proposal will investigate feasibility of this approach. Through the implementation and use of a micro-grid test bed, the PIs propose to analyze the feasibility of fusion of digital data and high-voltage signals and demonstrate seamless integration of alternative energy sources (e.g., solar energy) with the micro-grid while using the CDG's framework. The PIs will also study methods for allocation and distribution of electrical power for a CDG framework. The novel concept ascertains targeted delivery of energy to specific users, thus simultaneously minimizing overall power overhead and conservation of non-renewable energy resources. This new grid will also increase robustness against failures and enable sudden resumption of service through proactive surge management, an ability to negotiate reduced power rates in return for lower consumption in real time, and an ability to route energy from alternative sources back to the grid without compromising grid stability.
Performance Period: 08/15/2016 - 07/31/2018
Institution: CUNY City College
Sponsor: National Science Foundation
Award Number: 1640715
EAGER: Underground Infrastructure Sensing, Mapping and Modeling for Smart Maintenance, Sustainability and Usage
Lead PI:
Dryver Huston
Abstract
This project researches novel 3-D mapping techniques for the pipes and conduits carrying water, sewer, electricity, gas, telecommunications and steam underneath cities. Much of this infrastructure is aging and in unknown condition with unknown locations. The research uses a smart cities approach to managing information regarding urban underground utility infrastructure, as part of the US Ignite/NIST Global Cities Team Challenge (GCTC), operating in the adjacent cities of Burlington and Winooski. This is a small metropolitan region that enables ease of implementation of research efforts, yet big enough to elucidate key issues affecting scaling up to larger cities. The overall approach examines sensing and information technology to determine the state of infrastructure and provide it in an appropriate, timely and secure format for the managers, planners and users. The sensors include advanced ground penetrating radar and leak sensors connected to a high-speed network. Information will flow to and from a novel 3-D utility mapping and condition database. This information will be of use for presentation in graphical format for mobile and fixed devices, and for use by urban planners and engineers, along with maintenance and construction personnel. This research addresses mapping and information processing of the location and condition of underground urban infrastructure. The primary research objectives are: 1. Urban underground 3-D utility mapping and sensor network database - This will be a novel system that integrates Building Information Modeling (BIM) and Geographical Information (GIS) database techniques; 2. Innovative high-speed tomographic ground penetrating radar (GPR) system - Imaging and assessing buried features is hampered by the congested nature of urban underground infrastructure. The research will examine advanced multi-static and phased array GPR techniques to measure scattering from pipes off-axis and to steer radar waves; 3. Underground water pipe monitoring sensor system - The design of the sensor network requires determining how to configure a heterogeneous mix of acoustic, pressure and flow metering sensors onto a high-speed fiber optic telemetry network, to detect, locate and assess leaks in fresh water supply systems. Prototyping experiments will start with acoustic sensors; 4. Digital and automated analysis tools - The GPR and sensor network data streams are multichannel, complicated and voluminous. Automated data processing methods are required for mapping and condition assessment, and 5. Field test research - Measure the performance of the utility mapping and sensor network database, associated instrumentation and analysis in the adjacent cities of Burlington and Winooski, VT.
Performance Period: 09/01/2016 - 08/31/2018
Institution: University of Vermont & State Agricultural College
Sponsor: National Science Foundation
Award Number: 1640687
Increasing Healthcare Access to At-Risk Populations: Research-based Policies for Mobile Health Clinics
Lead PI:
Rigoberto Delgado
Abstract
IIS-1637347 Increasing Healthcare Access to At-Risk Populations:Research-based Policies for Mobile Health Clinics Project Description Mobile clinics play an important role in providing healthcare to at-risk populations in both urban and rural areas. Currently, over 1500 mobile clinics operate in the US and handle over 5 million visits per year providing essential healthcare services. These programs, however, have grown organically over time and it is important to establish evidence-based approaches to encourage a systematic expansion of this essential healthcare delivery model. This proposal addresses this issue through the use of epidemiological and economic GIS data, combined with proprietary routing software, to implement a program for optimal delivery of mobile clinic healthcare services in the Houston, Texas, region. The project involves eight different providers and includes designing systematic strategies for meeting future healthcare needs of low-income communities. The aim is also to develop models and techniques that can be implemented by mobile clinic programs throughout the country. Early estimates indicate that this project can result in 20% increase in mobile health clinic capacity, which could translate into significant savings in healthcare costs, and considerable improvements in quality of life for the poor. These results are in line with the NSF mission goal of promoting the advancement of health. The overall goal of this project is to optimize and implement a data-based program for coordinated deployment of mobile clinic programs. We will initially identify optimal expansion strategies for the eight current mobile clinics programs to meet the fast-growing demand for healthcare services in underserved communities. The project will then measure the potential of the developed models and techniques and apply them to mobile clinic programs in other regions of Texas and other states in the nation. To achieve the above goals, the team will first apply data mining and forecasting techniques to estimate present and future demand of healthcare services in selected communities. We will combine these approaches with advance GIS mapping tools and stochastic measures to identify target population clusters. We will also conduct survey and economic analysis to measure the operational cost and identify operational constraints in the present mobile clinic programs, and develop new optimization models for the deployment of the mobile clinic service. Then, we will develop new effective techniques to solve the developed optimization models to estimate the maximum capacity of the current mobile health clinic programs in the Houston region, and identify expansion strategies to meet the future service demand while minimizing cost. Lastly, we will estimate overall healthcare cost savings and quality of life impact at the community level at baseline and post-intervention.
Institution: University of Texas Health Science Center Houston
Sponsor: National Science Foundation
Award Number: 1637347
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