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.

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.

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.

Device authentication and identification has been recently cited as one of the most pressing security challenges facing the Internet of things (IoT). In particular, the open-access nature of the IoT renders it highly susceptible to insider attacks. In such attacks, adversaries can capture or forge the identity of the small, resource constrained IoT devices and, thus, bypass conventional authentication methods. Such attacks are challenging to defend against due to the apparent legitimacy of the adversaries' devices. The primary goal of this research is to overcome this challenge by developing new authentication methods that supplement traditional security solutions with cyber-physical fingerprints extracted from the IoT devices' environment. This project will develop a novel machine learning framework that enables the IoT to dynamically identify, classify, and authenticate devices based on their cyber-physical environment and with limited available prior data. This will result in the creation of environment-based IoT device credentials that can serve as a means of attestation, not only on the legitimacy of a device's identity, but also on the validity of the physical environment it claims to monitor and the actions it claims to be performing over time. The framework will also encompass an experimental IoT software platform that will be built to validate the proposed research. Owing to a partnership with the NIST Global City Teams Challenge (GCTC) project "Bringing Internet of Things Know-How to High School Students", a collaboration with IoT-DC, Arlington County, VA, and other entities, the proposed research will train high school students, STEM educators, and a broad community on a variety of research topics that will include IoT security, cyber-physical systems, and data analytics. The broader impacts will also include the creation of an interdisciplinary workforce focused on securing tomorrow's smart cities.

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.

This project will work with national and international medical and disaster professionals to extract formal use cases for ground, aerial, and marine robots for medical response and humanitarian relief to the Ebola (and future) epidemics. A set of detailed use cases is urgently needed to meet the challenges posed by the epidemic and to prepare robotics for assisting with future epidemics. The robotics community cannot provide robots without understanding the needs and engineering mistakes or mismatches will both be financially costly and delay the delivery of effective solutions. This is a rare opportunity to work with responders as they plan for a deployment of more than 3,000 troops plus Centers for Disease Control workers, and a possibly greater number of volunteers through non-governmental organizations such as Doctors Without Borders. The project outcomes will allow robotics companies to confidently pre-position/re-position products and to incorporate the findings into R&D investment strategies. The categorization of problems will guide academia in future research and to use as motivating class projects. The effective use of robots will provide responders with tools for the short term and will provide achievable expectations of robotics technology in general. There is no comprehensive statement of the missions that robots can be used for during a medical event and general mission descriptions (e.g. we need a robot to transport bodies) do not capture the design constraints on a robot. Prior work has shown that not understanding the operational envelope, work domain, and culture results in overly expensive robots that cannot be adopted. Robotics has not been considered by health professionals for the entire space of a medical event (hospitals, field medicine, logistics, security from riots), nor has the disaster or medical robotics communities been engaged with epidemics. This project will provide the fundamental understanding of how robots can be used for medical disasters and will design a formal process for projecting robotics requirements. It will benefit safety security and rescue robotics by expanding research from meteorological, geological, and man-made disasters to medical disasters and surgical robotics and telerobotics by pushing the boundaries of how robots are used for biosafety event.

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