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.

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.

Three emerging technologies provide unique opportunities for denser cities throughout the developed world: vehicle sharing, electric vehicles, and autonomous systems. Bringing these technologies close together can help enable joint mobility-on-demand and urban-logistics services. This project focuses on the co-development of design and algorithms to enable new concepts that will serve this purpose. The Persuasive Electric Vehicle (PEV) is a tricycle navigating in the bike lanes. The PEV can autonomously drive itself to its next customer; it can also deliver packages to its customers who order goods online. On the algorithmic front, the project will investigate (i) provably-safe algorithms for autonomous navigation in bike lanes, and (ii) algorithms for high-performance routing and rebalancing for joint mobility on demand and urban logistics. On the design front, the project will investigate (i) the vehicle-level designs that can best embrace the relevant CPS technologies, and (ii) the system-level designs and urban planning practices that can help enable the PEV concept. The PIs will collaborate with the City of Boston and participate in the Global City Teams Challenge, where they will demonstrate the PEV concept and its potential impact on future smart cities.

This project exploits an early concept of a flexible, low-cost, and drone-carried broadband long-distance communication infrastructure and investigates its capability for immediate smart-city application in emergency response. This effort is to support the Smart Emergency Response System (SERS) cluster to participate in the Global City Teams Challenge. This project will have an immediate impact in firefighting and other smart-city emergency response applications by quickly deploying a broadband communication infrastructure, thus improving the efficiency of first responders and saving lives. This communication infrastructure expands the capability of individual drones and enables broad new multi-drone applications for smart cities and has the potential to create new businesses and job markets. This interdisciplinary project addresses the following technology issues: 1) development of cyber-physical systems (CPS) technology that enables robust long-range drone-to-drone communication infrastructure; 2) practical drone system design and performance evaluation for WiFi provision; and 3) a systematic investigation of its capability to address smart-city emergency response needs, through both analysis and participation in fire-fighting exercises, as a case study. The project team includes an academic institution, technology companies and government planners, each of whom provides complementary expertise and perspectives that are crucial to the success of the project. The project also provides exciting interdisciplinary training opportunities for students and the community to learn CPS technologies and the Global City Teams Challenge efforts.

Human-in-the-loop control strategies in which the user performs a task better, and feels more confident to do so, is an important area of research for cyber-physical systems. Humans are very adept at learning to control complex systems, particularly those with non-intuitive kinematic constraints (e.g., cars, bicycles, wheelchairs, steerable needles). With the advent of cyber-physical systems, (physical systems integrated with cyber control layer), human control is no longer constrained to system inputs. Users can also control system outputs through a number of different teleoperation mappings. Given all this flexibility, what is the most intuitive way for a human user to control an arbitrary system and how is intuitiveness quantified? The project focuses on human-in-the-loop control for medical needles, which steer with bicycle-like kinematics. These needles could be used in a variety of medical interventions including tissue biopsy, tumor ablation, abscess drainage, and local drug delivery. We have explored a variety of teleoperation mappings for human control of these steerable needles; yet, we have found inconsistencies between objective performance metrics (e.g., task time and error), and post-experimental surveys on comfort or ease-of use. Users occasionally report a preference for control mappings, which objectively degrade performance, and vice versa. It is important to measure the real-time engagement of the user with the physical system in order to capture the nuances of how different control mappings affect physical effort, mental workload, distraction, drowsiness, and emotional response. Physiological sensors such as electroencephalography (EEG), galvanic skin response (GSR), and electromyography (EMG), can be used to provide these real-time measurements and to quantitatively classify the intuitiveness of new teleoperation algorithms. Broader Impacts: Intuitive and natural human-in-the-loop control interfaces will improve human health and well being, through applications in surgery and rehabilitation. The results of this study will be disseminated publicly on the investigator's laboratory website, a conference workshop, and a new medical robotics seminar to be held jointly between UT Dallas and UT Southwestern Medical Center. Outreach activities, lab tours, and mentoring of underrepresented students at all levels, will broaden participation in STEM. Additionally, the proximity of the investigator?s hospital-based lab to medical professionals will engage non-engineers in design and innovation

It is now recognized that cloud data centers are a significant consumer of energy resources and a substantial source of greenhouse gas emission. On the other hand, the intermittency and uncertainty of renewable energy present a daunting operating challenge for the electricity grid. The key idea behind this CRII: Cyber-Physical Systems project is that these two challenges are in fact symbiotic: data centers can be virtual batteries for the electricity grid. Specifically, data centers are large loads, but are also flexible. If the electricity grid can call on the flexibilities of data centers via appropriate demand response programs, this will be a crucial tool for easing the incorporation of renewable energy into the electricity grid. Unfortunately, despite the great potential, the current reality is that data centers perform little demand response. This project aims at the interdisciplinary challenges of enabling demand response from cloud data centers to realize the societal benefits. The overarching goal of this project is to develop an intellectual framework to understand and guide the realization of demand response from cloud data centers, to address engineering and economic challenges in order to manage the daunting risk. This project will first quantify the potential economic and environmental benefits of demand response from cloud data centers. The quantification includes the societal cost savings and emission reductions from networked data centers through geographical load balancing, and the impacts of demand response taxonomy. Built upon the first thrust, this project will continue to tackle the interdisciplinary challenges of both local control algorithm design and global market design for data center demand response in order to facilitate their participation in various demand response programs. The researchers will study prediction-based pricing design and analysis, demand response program design based on optimization decomposition, and distributed online algorithm design for risk management and distributed control. The results of this project will, at the societal level, help utility companies and load serving entities realize the great potential that lies in the Cloud, and, furthermore, design demand response programs that provide right incentives for data center operators to participate. At a local level, this project will help guide the management of geographically distributed data centers in participating in the right demand response programs. The control algorithms and demand response programs, as well as the methodology, can be applied beyond data centers. This research will create new knowledge in distributed online algorithm design and optimization-based market design. Additionally, this project will help design an interdisciplinary course Sustainable IT and IT for Sustainability. Personnel involved in this project, graduates and undergraduates, will receive innovation experiences through the algorithm design, analysis, implementation, and testing.

This Data-driven Decision-making in Cyber-physical systems (CPS) project focuses on bringing tools from data science and systems science together to develop new tools for analyzing and making accurate decisions in complex cyber-physical systems (e.g., power-grid, transportation network, power plants and smart buildings) to make them safer, more efficient and highly secure. This project develops algorithms, implements software and demonstrates proof-of-concept using large integrated building system as a challenge application area. Potential advantages of the tools developed in this research over current methods will be higher degree of accuracy, increased automation and lower cost of implementation. Majority of state-of-the-art methods use ad-hoc rules and physics-based models for such problems. However, they lack in accuracy and scalability due to the very complex nature of current and future large interconnected systems. The tools developed in this project will alleviate these issues significantly via intelligent use of large volume of data generated from the systems. The theoretical aspect of the research will make use of inherently multidisciplinary concepts from Nonlinear Dynamics, Information Theory, Machine Learning and Statistical Mechanics. The research project primarily supports interdisciplinary education and career development of graduate students as well as offers education and outreach programs to high school and undergraduate students in STEM disciplines. The project engages the Center for Building Energy Research (CBER) at Iowa State to demonstrate success on a real platform. The center provides a unique opportunity to the researchers to test and validate the tools on the Interlock House test bed which is a high end field laboratory for energy efficiency research and data validation. This enhances the potential of transitioning the new technology toward commercial reality.Soumik

This proposal will establish a framework for developing distributed Cyber-Physical Systems operating in a Networked Control Systems (NCS) environment. Specific attention is focused on an application where the computational, and communication challenges are unique due to the sheer size of the physical system, and communications between system elements include potential for significant losses and delays. An example of this is the power grid which includes large-scale deployment of distributed and networked Phasor Measurement Units (PMUs) and wind energy resources. Although, much has been done to model and analyze the impact of data dropouts and delay in NCS at a theoretical level, their impact on the behavior of cyber physical systems has received little attention. As a result much of the past research done on the `smart grid' has oversimplified the `physical' portion of the model, thereby overlooking key computational challenges lying at the heart of the dimensionality of the model and the heterogeneity in the dynamics of the grid. A clear gap has remained in understanding the implications of uncertainties in NCS (e.g. bandwidth limitations, packet dropout, packet disorientation, latency, signal loss, etc.) cross-coupled with the uncertainties in a large power grid with wind farms (e.g. variability in wind power, fault and nonlinearity, change in topology etc.) on the reliable operation of the grid. To address these challenges, this project will, for the first time, develop a modeling framework for discovering hitherto unknown interactions through co-simulation of NCS, distributed computing, and a large power grid included distributed wind generation resources. Most importantly, it addresses challenges in distributed computation through frequency domain abstractions and proposes two novel techniques in grid stabilization during packet dropout. The broader impact lies in providing deeper understanding of the impact of delays and dropouts in the Smart Grid. This will enable a better utilization of energy transmission assets and improve integration of renewable energy sources. The project will facilitate participation of women in STEM disciplines, and will include outreach with local Native American tribal community colleges This project will develop fundamental understanding of impact of network delays and drops using an approach that is applicable to a variety of CPS. It will enable transformative Wide-Areas Measurement Systems research for the smart grid through modeling adequacy studies of a representative sub-transient model of the grid along with the representation of packet drop in the communication network by a Gilbert model. Most importantly, fundamental concepts of frequency domain abstraction including balanced truncation and optimal Hankel-norm approximation are proposed to significantly reduce the burden of distributed computing. Finally, a novel `reduced copy' approach and a `modified Kalman filtering' approach are proposed to address the problem of grid stabilization using wind farm controls when packet drop is encountered.

This CAREER project responds to an urgent need to develop mobile power distribution systems that lower deployment and operating costs while simultaneously increasing network efficiency and response in dynamic and often dangerous physical conditions. The significant need for an efficient and effective mobile power distribution system became evident during search and rescue/recovery missions following the Japan tsunami and the disappearance of the Malaysia MH370 airplane. The technology outcomes from this project will apply to a broad range of environments (in space, air, water or on ground) where the success of long-term robotic network missions is measured by the ability of the robots to operate, for an extended period of time, in highly dynamic and potentially hazardous environments. These advanced features will provide the following advantages: efficiency, efficacy, guaranteed persistence, enhanced performance, and increased success in search/rescue/recovery/discovery missions. Specifically, this project addresses the following technology problems as it translates from research discovery toward commercial application: inflated energy use currently required when the autonomous vehicles break from mission to return to recharging station; lack of multi-robot coordination needed to take into account both fundamental hardware and network science challenges necessary to respond to energy needs and dynamic environment conditions. By addressing these gaps in technology, this work establishes the theoretical, computational, and experimental foundation for mobile power delivery and onsite recharging capability. Moreover, the new technology developed in this project is universally adaptable for disparate autonomous vehicles especially autonomous underwater vehicles (AUVs). In more technical terms, this project creates network optimization and formation strategies that will enable a power distribution system to reconfigure itself depending on the number of operational autonomous vehicles and recharging specifications to meet overall mission specifications, the energy consumption needs of the network, situational conditions, and environmental variables. Such a system will play a vital role in real-time controlled applications across multiple disciplines such as sensor networks, robotics, and transportation systems where limited power resources and unknown environmental dynamics pose major constraints. In addition to addressing technology gaps, undergraduate and graduate students will be involved in this research and will receive interdisciplinary education/ innovation/ technology translation/ outreach experiences through: developing efficient network energy routing, path planning and coordination strategies; designing and creating experimental test-beds and educational platforms; and engaging K-12th grade students in Science, Technology, Engineering and Math including those from underrepresented groups. This project engages Michigan Tech's Great Lake Research Center (GLRC) and Center for Agile Interconnected Microgrids (AIM) to develop experimental test-beds and conduct tests that validate the resulting methods and algorithms, and ultimately, facilitate the technology translation effort from research discovery toward commercial reality.