A wide range of health outcomes is affected by air pollution. In March 2014 the World Health Organization (WHO) released a report that in 2012 alone, a staggering 7 million people died as a result of air pollution exposure, one in eight of total global deaths. A major component of this pollution is airborne particulate matter, with approximately 50 million Americans have allergic diseases. This project will develop and field the first integrated IoT in-situ sensor package tracking pollution and pollen to provide airborne particulate mapping for Chattanooga. Longer term it is hoped that the data collection approach and initial visualization tools developed in Chattanooga can be used to support a nationwide, open access dissemination platform on the order of Google's StreetView, but called PollutionView. Such scaling of the project's pilot results through a PollutionView tool will contribute significantly to a transformation of the Environmental Public Health field in the United States. The project involves real-time big data analysis at a fine-grain geographic level. This will involve trades with sensing and computing especially if the sensor package is to be deployed at scale. The project will help determine if real-time allergen collection and visualization can improve health and wellness. Thus, this project will combine Cyber Physical Systems (CPS) and gigabit networks to address major health concerns due to air pollution. A working demonstration of this project will be presented during the Global City Teams meeting in June 2015 with an update in June 2016. Airborne particulate matter particularly affects the citizens of Chattanooga, TN. The objectives of this project are twofold: first, to develop and deploy an array of Internet of Things (IoT) in-situ sensors within Chattanooga capable of comprehensively characterizing air quality in real time, including location, temperature, pressure, humidity, the abundance of 6 criterion pollutants (O3, CO, NO, NO2, SO2, and H2S), and the abundance of airborne particulates (10-40 µm), both pollen-sized and smaller PM2.5 (<2.5 µm) particles; and second, to have a pollen validation campaign by deploying an in-situ pollen air sampler in Chattanooga to identify specific pollen types.

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.

Cyber physical systems (CPSs) are merging into major mobile systems of our society, such as public transportation, supply chains, and taxi networks. Past researchers have accumulated significant knowledge for designing cyber physical systems, such as for military surveillance, infrastructure protection, scientific exploration, and smart environments, but primarily in relatively stationary settings, i.e., where spatial and mobility diversity is limited. Differently, mobile CPSs interact with phenomena of interest at different locations and environments, and where the context information (e.g., network availability and connectivity) about these physical locations might not be available. This unique feature calls for new solutions to seamlessly integrate mobile computing with the physical world, including dynamic access to multiple wireless technologies. The required solutions are addressed by (i) creating a network control architecture based on novel predictive hierarchical control and that accounts for characteristics of wireless communication, (ii) developing formal network control models based on in-situ network system identification and cross-layer optimization, and (iii) designing and implementing a reference implementation on a small scale wireless and vehicular test-bed based on law enforcement vehicles. The results can improve all mobile transportation systems such as future taxi control and dispatch systems. In this application advantages are: (i) reducing time for drivers to find customers; (ii) reducing time for passengers to wait; (iii) avoiding and preventing traffic congestion; (iv) reducing gas consumption and operating cost; (v) improving driver and vehicle safety, and (vi) enforcing municipal regulation. Class modules developed on mobile computing and CPS will be used at the four participating Universities and then be made available via the Web.

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.

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).

Millions of mobile applications (apps) are being developed in domains such as energy, health, security, and entertainment. The US FDA expects that there will be 500 million smart phone users downloading healthcare related apps by the end of 2015. Many of these apps will perform interventions to control human physiological parameters such as blood pressure and heart rate. The intervention aspects of the apps can cause dependency problems, e.g., multiple interventions of multiple apps can increase or decrease each other's effects, some of which can be harmful to the user. Detecting and resolving these dependencies are the main goals of this project. Success in this research can significantly improve the safety of home health care. This project will develop EyePhy, a completely new approach to primary and secondary dependency analysis for wellness and mobile medical apps based on smart phones. The approach offers personalized dependency analysis and accounts for time dependent interventions such as time interval for which a drug or other intervention is effective. To do that, EyePhy uses a physiological simulator called HumMod which was developed by the medical community to model the complex interactions of the human physiology using over 7800 variables. Among the goals of EyePhy are the reduction of app developers' effort in specifying dependency metadata compared to state of the art solutions, offering personalized dependency analysis for the user, and identifying problems in real time, as medical app products are being used. Such dependency problems occur mainly because (i) each app is developed independently without knowing how other apps work and (ii) when an app performs an intervention to control its target parameters (e.g., blood pressure), it may affect other physiological parameters (e.g., kidney) without even knowing it. A priori proofs that individual cyber-physical systems (CPS) app devices are safe cannot guarantee how it will be used and with which other (future) apps it may be run concurrently. It is becoming more common for people to use multiple apps. The average person will not understand how multiple apps might affect his health due to hidden dependencies among a large number of parameters. Consequently, a tool such as EyPhy is critical to future deployments of safe mobile medical apps.

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.