Water is a critical resource and a lifeline service to communities worldwide; the generation, treatment, distribution and maintenance of water workflows is typically managed by local governments and water districts. Recent events such as water supply disruptions caused by Hurricane Sandy in 2012 and the looming California drought crisis clearly indicate society's dependence on critical lifeline services such as water and the far-reaching impacts that its disruption can cause. Over the years, these critical infrastructures have become more complex and often more vulnerable to failures. The ability to view water workflows as a community wide cyber-physical system (CPS) with multiple levels of observation/control and diverse players (suppliers, distributors, consumers) presents new possibilities. Designing robust water systems involves a clear understanding of the structure, components and operation of this CPS system and how community infrastructure dynamics (e.g. varying demands, small/large disruptions) impact lifeline service availabilities and how service level decisions impact infrastructure control. The proposal emphasizes a new approach to exploring engineering systems that will result in substantial advances in the understanding of lifeline systems and approaches to make them adaptive and resilient. Building resilience into urban lifelines raises a number of monumental challenges including identifying the aspects of systems that can be observed/sensed and adapted and to developing general principles that can guide adaptation. The key idea is to develop methodologies to understand operational performance and resilience issues for real-world community water infrastructures and explore solutions to problems in cyberspace before instantiating them into a physical infrastructure. The effort includes: 1) Developing a flexible modeling framework that captures system needs at multiple levels of temporal and spatial abstraction; 2) Developing real-time algorithms supporting resilience; 3) Designing adaptations for water systems using a data-driven approach; and 4) Demonstrating the important broader impact of the research on critical water system infrastructure at the Global City Technology Challenge and the longer term impact on infrastructure for a resilient control framework.

Water is a critical resource and a lifeline service to communities worldwide; the generation, treatment, distribution and maintenance of water workflows is typically managed by local governments and water districts. Recent events such as water supply disruptions caused by Hurricane Sandy in 2012 and the looming California drought crisis clearly indicate society's dependence on critical lifeline services such as water and the far-reaching impacts that its disruption can cause. Over the years, these critical infrastructures have become more complex and often more vulnerable to failures. The ability to view water workflows as a community wide cyber-physical system (CPS) with multiple levels of observation/control and diverse players (suppliers, distributors, consumers) presents new possibilities. Designing robust water systems involves a clear understanding of the structure, components and operation of this CPS system and how community infrastructure dynamics (e.g. varying demands, small/large disruptions) impact lifeline service availabilities and how service level decisions impact infrastructure control. The proposal emphasizes a new approach to exploring engineering systems that will result in substantial advances in the understanding of lifeline systems and approaches to make them adaptive and resilient. Building resilience into urban lifelines raises a number of monumental challenges including identifying the aspects of systems that can be observed/sensed and adapted and to developing general principles that can guide adaptation. The key idea is to develop methodologies to understand operational performance and resilience issues for real-world community water infrastructures and explore solutions to problems in cyberspace before instantiating them into a physical infrastructure. The effort includes: 1) Developing a flexible modeling framework that captures system needs at multiple levels of temporal and spatial abstraction; 2) Developing real-time algorithms supporting resilience; 3) Designing adaptations for water systems using a data-driven approach; and 4) Demonstrating the important broader impact of the research on critical water system infrastructure at the Global City Technology Challenge and the longer term impact on infrastructure for a resilient control framework.

The concept of a "smart city" is ubiquitous with data; however, most urban data today lacks the spatial and temporal resolution to understand processes that unfold on timescales of seconds or minutes, such as the dispersion of pollutants. A better understanding of these dynamics can provide information to residents, cyclists or pedestrians who may wish to use air quality data as they navigate urban spaces. This project leverages existing street furniture, integrating air quality and environmental sensors into commercial solar powered, networked waste stations. Sensors embedded in BigBelly waste stations in Chicago and other cities will collect data that will allow researchers to explore critical questions that must be understood in order to begin to develop and drive policies, measurement strategies, and predictive computational models related to the feedback loop between traffic flow and air quality. The partnership with BigBelly, with nearly 30,000 waste stations in place globally, provides a channel through which sensors can be deployed in many cities. The project brings together computer science, cyber-physical systems, distributed systems, and sensor systems expertise to explore technical and societal challenges and opportunities of urban-scale embedded systems in the public sphere, initially related to understanding and ultimately managing urban air quality. Sensors embedded in BigBelly waste stations in Chicago and other cities will explore (1) the spatial and temporal dynamics of air quality in urban canyons, informing the sensor network resolution needed to drive traffic change policies and to provide healthy air quality routing information to cyclists and pedestrians; and (2) how urban topology (natural and built) affects these dynamics and associated required measurement resolutions. These are critical questions that must be understood in order to begin to develop and drive policies, measurement strategies, and predictive computational models related to the feedback loop between traffic flow and air quality. Critical challenges include (1) power management with respect to sensor sampling, in-situ processing, and transmission; (2) ensuring data quality; and (3) providing data in forms that are actionable and understandable to policy makers and the general public. All data will be published in near-real time with web-based analysis tools for use by scientists, educators, policy makers, and residents, and with application programming interfaces (API's) for application development. By developing an open source, readily deployed urban embedded systems infrastructure leveraging a widely deployed commercial platform, the project can enable science, education, and outreach in many cities, national parks, and educational institutions worldwide.

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.

Many critical infrastructures, such as the power grid, are complex cyber physical systems (CPS). Protecting these systems against cyber-attacks is of paramount importance to national security and economic well-being. Risk assessment considering cyber-attacks against critical infrastructures is not well understood due to ever growing, dynamic threat landscape coupled with complex cyber-physical interactions in these systems. In addition, there is a compelling need to create environments in which realistic attack-defense experiments (including risk assessment and risk mitigation) and training exercises can be safely conducted to advance the science and workforce development in this important area of national need. This project has two key goals: (1) the short-term goal is to design, develop, and demonstrate a cyber defense exercise for improving the security of CPS systems in alignment with the NIST/US Ignite Global Cities Team Challenge; and (2) the long-term goal is to explore fundamental models and algorithms for cyber risk assessment and mitigation. The project makes synergistic federation of three existing security testbeds hosted at Iowa State University and the University of Southern California to create a realistic environment for conducting CPS security experimentation and security preparedness and training exercise, like the North American Reliability Corporation (NERC) GridEx. The intellectual merit of the project lies in two key contributions: (i) realistic experimentations on CPS security testbed federation, and (ii) the development of a novel methodology for cyber risk modeling of CPS systems. The broader impacts of the project lie in developing realistic attack-defense scenarios and learning/training modules that enable academic researchers, students, and industry practitioners to systematically understand, analyze, and improve the security and resiliency of critical infrastructures.

Smart Cities are complex cyber-physical systems with large human populations adding additional complexity. Instrumentation and modeling are components of a smart city. Regardless, however, of the ubiquity of instrumentation and precision of models, in the end, humans and human teams will make decisions about citywide operations and management, especially in crisis. We contend that the hierarchical nature of contemporary command and control systems can create virtual blind spots in which opportunities or dangers may be invisible to the hierarchy because the necessary information is obscured as it moves between levels of abstraction in the hierarchy. This project will involve teaming with crisis management experts and researchers to develop intelligent agents designed to minimize cognitive load on decision makers, exploit semantic relationships in reports and sensor data to advise of otherwise invisible occurrences, and sequence the actions of ground-level assets to refine causal relationship models to better reflect ongoing developments during crisis and/or event management. This project addresses the following technology gap(s) as it translates from research discovery toward commercial application - a) demonstration of the effectiveness of information presentation and transparency in situations where agents can support and enhance human decision-making without increasing the cognitive workload of the human; b) transfer state-of-the-art foundational research in semantic data and information integration to the complex disaster scenario; c) development of model consistency maintenance tools for automatic update of causal models of various disaster and/or emergency situations. In addition, personnel involved in this project, e.g., graduate students, will receive innovation experiences through the design, development and testing of the model developed. This project will explore transferability of the research results into tools in other application areas such as Pararescuer training, AFRL disaster response system RIPPLE, and Clark County Emergency Management Agency. This project will also have outreach efforts with mentoring high school and undergraduate students at Discovery Lab, Tec^Edge through the Summer at the Edge/Year at the Edge Programs (SATE/YATE).

It is expected that in 25 years, Americans who are 65 years or older will account for about 20% of the whole population. As smart cities are also expected to become a reality within the same timeframe, starting to address the needs and concerns of such a large group becomes an essential part of the design of a future smart city. Here we specifically address the mobility needs of the elderly and those with limited means of transportation. We consider multiple small vehicle options that might provide on-demand or scheduled means of door-to-door transportation. The NSF-EAGER project focuses on examining basic research aspects of sensing and tracking potential sources of vehicle pedestrian collisions in densely crowded situations and socially acceptable distance for collision avoidance. The project will be providing input to the OSU/Columbus Global City Teams Challenge activity SMOOTH (Smart Mobile Operation: OSU Transportation Hub) and related demonstrations and help develop a working system. The key innovative contributions of this EAGER project are: development of a unifying framework for sensing and tracking in mixed traffic situations, acceptable automated driving within pedestrian zones, and evasive road maneuvering to avoid colliding with conventional human driven vehicles.

The focus of this project is on creating new techniques for understanding population analytics over a space of interest, e.g., a shopping mall, a busy street, or an entire city. Knowledge of population behavior important for many applications. For instance, knowledge of which are the busy corners of city sidewalk can provide city planners with input on where to invest city resources. Knowledge of where people congregate in a shopping mall allows officials to plan where to provide useful services, e.g., information kiosks, floor plans, and more. The process of gathering population analytics today is tedious -- some stores and shops use manual people counters to track how many persons are entering wireless technologies. The technical contributions of this project are two-fold. First, it is attempting to reduce the complexity of determining location of people by reducing the number of infrastructure points needed. Second, automated approaches to population analytics are fraught with privacy concerns, and this project is examining techniques that mitigate such concerns. Personnel involved in this project will be trained in significant technical skills across a broad set of domains including wireless technologies, privacy techniques, and machine learning. To demonstrate the feasibility of this project, the PI team is deploying a version of the system in an urban downtown area of Madison, WI. The team is collaborating with a number of local partners -- the city of Madison, the University of Wisconsin Bookstore, 5NINES (a local Internet Service Provider), and a few local participants. Together they are entering this technology demonstration as part of the Global City Teams Challenge being hosted by NSF and NIST.

Large-scale applications of cyber-physical systems (CPS) such as commercial buildings with Building Automation System (BAS)-based demand response (DR) can play a key role in alleviating demand peaks and associated grid stress, increased electricity unit cost, and carbon emissions. However, benefits of BAS alone are often limited because their demand peak reduction cannot be maintained long enough without unduly affecting occupant comfort. This project seeks to develop control algorithms to closely integrate battery storage-based DR with existing BAS capabilities. The overarching objective is to expand the building's DR capabilities, providing crucial benefits towards smarter grids, while maintaining appropriate occupant comfort and reducing building ownership cost. This project follows a 2-phase approach towards more effective integration. First, building peak demand forecasting will be added to existing battery dispatch methods. Under electricity tariffs geared towards daily [monthly] peaks, such forecasting could result in the same battery-enabled demand charge (dollars per kW) savings as previously demonstrated storage dispatch algorithms. However, supply charges (dollars per kWh) and associated emissions would be reduced because battery dispatch would be geared towards reducing only the biggest daily [monthly] peaks while not incurring roundtrip charging losses on more moderate peaks. Phase 2 builds on phase 1, adding closer integration and systematic optimization to the algorithms for forecasting, BAS, and battery dispatch. This integration will allow the integrated CPS to manipulate the BAS process itself, thereby optimizing, e.g., light dimming, temperature set-points, and pre-cooling in unison with battery-based DR. Feasibility and future promise of such experimental control methodology will be measured by a multi-objective cost function which includes demand and energy charges, savings from DR participation, storage equipment capital expenditure (required size, achievable lifetime), and occupant comfort. Integrating BAS- with battery-based DR is nascent, mostly because the peak demand forecast, BAS, and storage dispatch algorithms that such a CPS requires have yet to be developed. This project seeks to lay important methodological groundwork for such applications, thus furthering commercial buildings' role in the Internet of Things. The PI's participation in the NIST/US-Ignite Global City Team Challenge (with partners Urban Electric Power, Siemens Corporate Technology, City University of New York, and NY-Best) furthers public engagement with such technology and will help catalyze its translation into the commercial space.

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.