Smart cities, connected vehicles, smart homes, and connected healthcare devices are examples of how the Internet of Things (IoT) are expected to revolutionize our lives in the decades ahead by exploiting a wealth of user-specific data to significantly improve user experiences. However, sharing of such data can compromise a user's privacy, and this threat to user-privacy has emerged as a critical risk to the widespread adoption of IoT. This highlights an important and fundamental challenge critical to the Science of Cyber-Physical Systems: even if IoT data is carefully anonymized, significant privacy leaks can occur due to the sheer amount of the data generated and the use of powerful mathematical techniques by an adversary to match current behavior with traces of past user behavior. This project will develop a systematic approach to understand the fundamental underpinnings of privacy in IoT systems, and develop provably private IoT implementations that are robust to uncertainties in the models. A key advantage of this approach is that it can achieve provable privacy, i.e., no algorithm can break the privacy of the user. The project also continues the team's education and engagement of a diverse set of students, including the significant involvement of undergraduate students in the research program, and creates and promotes free and open access educational materials. The technical problems considered in the project are organized into two main thrusts. In Thrust 1, the theoretical foundations for IoT privacy are built. The main goal is to obtain a fundamental understanding of the degree to which the utility of IoT approaches can be maintained while employing privacy-preserving mechanisms to provably prevent an adversary from compromising a user's privacy by matching a given trace to prior user behavior. Critical to this thrust is achieving robust and model independent design, i.e., achieving perfect privacy with the minimum sets of assumptions about the system and data models. In Thrust 2, to validate the theory and demonstrate the potential impact of the approach, the project leverages the domain expertise of the team to apply the results of Thrust 1 in connected vehicle applications. More generally, this will indicate the degree to which the data of a given user can be kept private from an interested adversary while still supporting the use of such services.

The goal of this research is to gain a fundamental understanding of the integrated actuation, embedded sensing, reactive control, and distributed control needs of a cyber-physical, synthetic, distributed sensing, soft and modular tissue (sTISSUE). Realizing this cyber-physical, physiological testbed will enable surgically relevant tasks, procedures, and devices to be much more refined ahead of animal testing, which can be dramatically reduced with such high-fidelity simulators. Furthermore, such simulators could open an entirely new approach to medical resident training that could not only improve surgical performance skills, but also establish a new paradigm in patient-specific surgical practice before the actual procedure. The proposed strategy will also harness the excitement surrounding autonomous systems, robotic control, and embedded sensing, and leverage it with the investigators' infrastructure for education innovation and outreach to provide new, inspirational educational experiences for students. This research program will formulate the techniques required for a synthetic tissue to autonomously sense and react to external stimuli, thereby replicating smooth muscle's sense and actuation capability. In essence, an autonomous tissue will be created that simulates in vivo behavior, while maintaining scalability and modularity. The intellectual merit of this research lies in 1) addressing current shortcomings in embedded sensing and actuation that ensure modularity and distributed control, 2) modeling the dynamics of, and creating global and distributed control strategies that account for, the unconventional in vivo environment requirements, and 3) enabling a paradigm-altering platform that will allow technology developers to both quickly and reliably apply this sTISSUE to numerous applications. More broadly, this research will establish a crucial body of knowledge needed for the design of synthetic tissue materials that integrate sensing, actuation, computation, and control. While the proposed approach includes the goal to transition the fundamental research into a gastrointestinal simulator, numerous other applications in the field of medicine and co-robotics exist. Finally, the proposed research in modularity and scalability design can broadly impact a number of other areas that would benefit from the developed novel methodologies in integrated sensing, actuation, computation and control.

This project aims to empower ordinary citizens to take charge in collecting real time environmental data that can be used to serve a common interest. The target application of the project is a cyber-physical system for detecting small amounts of explosive vapor in the air so as to protect large-area public gatherings. In this system, extremely low-cost explosive sensors, handed out free of charge, will be connected to the smart phones of the willing participants, effectively turning each one of them into a look-out sensor node. Although the primary application of the proposed cyber-physical system is explosive detection, problems with similar technical challenges include air pollution monitoring systems, pandemic prevention, fire and/or gas leak monitoring. Successful completion of this project will generate a plethora of new applications that target problems with the aim of reaching a common goal by leveraging computation, power, and communications capacities donated by willing participants. Understanding that interest in STEM (science, technology, engineering, math)-related careers begins in elementary school, the project will use a sensor-based system that aims at locating a heat source, as part of K-12 outreach activities that explore crowd-sourced data collection, processing, and scheduling. The researchers work regularly with undergraduate and high school students through Fulton Undergraduate Research Initiative (FURI), Barrett Summer Scholars (BSS), National Science Foundation-sponsored National Nanotechnology Infrastructure Network Research Experience for Undergraduates (NNIN REU), and Arizona State University's Summer Research Experience for High School Students program. Designing a system based on extremely low-cost sensors for crowd-sourced monitoring has several unique technical and scientific challenges that the project tackles. First, cost/power requirements and the need to detect tiny amounts of explosive vapor are at odds with each other. Second, the system will be designed to tolerate inevitable sensor inaccuracies and false positives/negatives in a stable manner. Third, since the entire system hinges on willing participation from the public, sensor operation will be made transparent to the user, and not create a negative user experience. Finally, privacy concerns of the users will be addressed by keeping them anonymous, and the security threats generated by this anonymity will be addressed.

The growth and expansion of cities since the mid 20th century has led to a strong dependency on private automobiles. During the last years, urban planners have started rethinking the mobility modes in a city and have finally realized that a truly sustainable transportation and urban environment in general, requires a shift to multimodal transportation. In the PittSmartLiving project, we view the shift to multimodal transportation in a holistic way. In particular, we will design, develop, deploy, and evaluate a platform that will integrate information from and align the incentives of all involved stakeholders (commuters, transport operators, and local businesses) towards increasing the utilization and quality of public transportation. The resulting Cyber-Physical system will (1) provide commuters with real-time information of arrival and utilization of all relevant options of public transit (e.g., bus, subway, shuttles, bikes, etc.), and (2) build a marketplace around multimodal mobility, where businesses can offer time-sensitive incentives connected to this transit information to nearby commuters (e.g., the next bus is too full, come in and enjoy $1 coffee). This has the potential to improve not only the overall ridership experience by balancing utilization across public transportation networks (e.g., shifting some of the demand away from the peak hours), but also to optimize customer flows in local businesses. Significant emphasis will be given to the development of mechanisms that will be able to deliver the required services while respecting the privacy expectations of the commuters. As part of this project, an unprecedented experimental infrastructure will be deployed (in the Oakland and Downtown areas of Pittsburgh) that will allow the PIs to identify a set of incentive mechanisms that can shift commuters to public transportation in a real urban environment. This is the first time that an urban core truly becomes a laboratory, where scientists and engineers can run experiments aimed at improving the quality of life of city-dwellers. The main expected technical contributions of this project can be summarized as follows. (1) Development of a holistic urban transportation system that balances utilization across both public transportation networks and local businesses, thus improving not only public transit but also general urban living. (2) Design and evaluation of the market mechanism that integrates and aligns the incentives of various stakeholders. (3) Shift of attention from temporal efficiency (i.e., fastest route) to more sustainable commuting (e.g., public transit, biking etc.) as well as commuting options geared towards the well-being of dwellers (e.g., "beautiful" routes, "clean" routes, "accessible" routes etc.)

This project aims to empower ordinary citizens to take charge in collecting real time environmental data that can be used to serve a common interest. The target application of the project is a cyber-physical system for detecting small amounts of explosive vapor in the air so as to protect large-area public gatherings. In this system, extremely low-cost explosive sensors, handed out free of charge, will be connected to the smart phones of the willing participants, effectively turning each one of them into a look-out sensor node. Although the primary application of the proposed cyber-physical system is explosive detection, problems with similar technical challenges include air pollution monitoring systems, pandemic prevention, fire and/or gas leak monitoring. Successful completion of this project will generate a plethora of new applications that target problems with the aim of reaching a common goal by leveraging computation, power, and communications capacities donated by willing participants. Understanding that interest in STEM (science, technology, engineering, math)-related careers begins in elementary school, the project will use a sensor-based system that aims at locating a heat source, as part of K-12 outreach activities that explore crowd-sourced data collection, processing, and scheduling. The researchers work regularly with undergraduate and high school students through Fulton Undergraduate Research Initiative (FURI), Barrett Summer Scholars (BSS), National Science Foundation-sponsored National Nanotechnology Infrastructure Network Research Experience for Undergraduates (NNIN REU), and Arizona State University's Summer Research Experience for High School Students program. Designing a system based on extremely low-cost sensors for crowd-sourced monitoring has several unique technical and scientific challenges that the project tackles. First, cost/power requirements and the need to detect tiny amounts of explosive vapor are at odds with each other. Second, the system will be designed to tolerate inevitable sensor inaccuracies and false positives/negatives in a stable manner. Third, since the entire system hinges on willing participation from the public, sensor operation will be made transparent to the user, and not create a negative user experience. Finally, privacy concerns of the users will be addressed by keeping them anonymous, and the security threats generated by this anonymity will be addressed.

This project aims to empower ordinary citizens to take charge in collecting real time environmental data that can be used to serve a common interest. The target application of the project is a cyber-physical system for detecting small amounts of explosive vapor in the air so as to protect large-area public gatherings. In this system, extremely low-cost explosive sensors, handed out free of charge, will be connected to the smart phones of the willing participants, effectively turning each one of them into a look-out sensor node. Although the primary application of the proposed cyber-physical system is explosive detection, problems with similar technical challenges include air pollution monitoring systems, pandemic prevention, fire and/or gas leak monitoring. Successful completion of this project will generate a plethora of new applications that target problems with the aim of reaching a common goal by leveraging computation, power, and communications capacities donated by willing participants. Understanding that interest in STEM (science, technology, engineering, math)-related careers begins in elementary school, the project will use a sensor-based system that aims at locating a heat source, as part of K-12 outreach activities that explore crowd-sourced data collection, processing, and scheduling. The researchers work regularly with undergraduate and high school students through Fulton Undergraduate Research Initiative (FURI), Barrett Summer Scholars (BSS), National Science Foundation-sponsored National Nanotechnology Infrastructure Network Research Experience for Undergraduates (NNIN REU), and Arizona State University's Summer Research Experience for High School Students program. Designing a system based on extremely low-cost sensors for crowd-sourced monitoring has several unique technical and scientific challenges that the project tackles. First, cost/power requirements and the need to detect tiny amounts of explosive vapor are at odds with each other. Second, the system will be designed to tolerate inevitable sensor inaccuracies and false positives/negatives in a stable manner. Third, since the entire system hinges on willing participation from the public, sensor operation will be made transparent to the user, and not create a negative user experience. Finally, privacy concerns of the users will be addressed by keeping them anonymous, and the security threats generated by this anonymity will be addressed.

The confluence of two powerful global trends, (1) the rapid growth of cloud computing and data centers with skyrocketing energy consumption, and (2) the accelerating penetration of renewable energy sources, is creating both severe challenges and tremendous opportunities. The fast growing renewable generation puts forth great operational challenges since they will cause large, frequent, and random fluctuations in supply. Data centers, on the other hand, offer large flexible loads in the grid. Leveraging this flexibility, this project will develop fundamental theories and algorithms for sustainable data centers with a dual goal of improving data center energy efficiency and accelerating the integration of renewables in the grid via data center demand response (DR) and workload management. Specifically, the research findings will shed light on data center demand response while maintaining their performance, which will help data centers to decide how to participate in power market programs. Further, the success of data center demand response will help increase renewable energy integration and reduce the carbon footprint of data centers, contributing to global sustainability. The PIs will leverage fruitful collaboration to eventually bring the research to bear on ongoing industry standardization and development efforts. The PIs teach courses spanning networks, games, smart grid and optimization, and are strongly committed to promoting diversity by providing research opportunities to underrepresented students. Built on the PIs expertise on data centers and the smart grid, this project takes an interdisciplinary approach to develop fundamental theories and algorithms for sustainable data centers. The research tasks are organized under two well-coordinated thrusts, namely agile data center DR and adaptive workload management. The strategies and decisions of data center DR will be made based on the workload management algorithms that balance quality of service and energy efficiency and determine the supply functions. The workload management algorithms will optimize quality of service under the electric load constraints imposed by DR accordingly. This project will make three unique contributions: (1) new market programs with strategic participation of data centers in DR, instead of passive price takers, (2) fundamental understanding of the impacts of power network constraints on data center DR and new distributed algorithms for solving optimal power flow with stochastic renewable supplies, and (3) high-performance dynamic server provisioning and load balancing algorithms for large scale data centers under time-varying and stochastic electric load constraints and on-site renewable generation.

Near-real-time water-quality monitoring in rivers, lakes, and water reservoirs of different physical variables is critical to prevent contaminated water from reaching the civilian population and to deploy timely solutions, or at least to issue early warnings so as to prevent damage to human and aquatic life. In order to make optimal decisions and "close the loop" promptly, it is necessary to collect, aggregate, and process water data in real time. Therefore, the goal of this project is to design a Cyber Physical System (CPS) where drones such as the Rutgers multi-medium Naviator, a Hybrid Unmanned Air/Underwater Vehicle (HUA/UV), and autonomous underwater robots (e.g., modified BlueROVs) can (i) first identify Regions of Interest (RoIs) and take measurements and well as, if needed, collect biosamples from them; (ii) and then, through collaborative information fusion and integration, perform in-situ transformation of these measurements/raw data into valuable information and, finally, into knowledge. To achieve the above goal, this project will need to solve the problem of uncertainties that arise in in-situ processing of data from sensors in any CPS. This project will provide greater autonomy and cooperation in CPSs and, at the same time, will improve scalability, reliability, and timeliness in comparison to traditional sensing systems. The challenges to achieve dynamic collaboration between local and cloud resources will be handled in Task 1, in which novel adaptive-sampling solutions that minimize the sampling cost of a RoI (in terms of time or energy expenditure) will also be developed. In Task 2, novel solutions will be designed to handle model uncertainties in the local resources due to the unpredictable behavior of computational models to input data and resources' availability. In Task 3, the project aims at developing a biosampler, i.e., "lab-on-robot", that uses in-situ measurements and communicates with the cloud resources to give results in real time on the water quality; also, new solutions to optimize the Naviator's current hybrid air/water multirotor platform/propulsion system will be designed in order for it to be able to carry and perform testing with the biosampler while also increasing its endurance. Finally, in Task 4, integrated field testing on the Raritan River, NJ, will be performed so as to validate the algorithms as well as to analyze their scalability (from an economical and feasibility perspective) and confidence/accuracy performance. Specifically, the Naviators will identify the RoIs via multimodal operations, i.e., in water and air; and then the BlueROVs (which, during the course of the project, will be made autonomous and will be modified to carry on-board water-quality sensors) will perform underwater adaptive sampling in each of those RoIs using the algorithms designed in Task 1. In terms of broader impacts, the collaboration between cloud and local resources can benefit any CPS in the following ways: (i) outsourcing computation to the cloud will allow resource-constrained vehicles (in terms of computational capability) to meet mission deadlines, and (ii) using clouds comes at a price, hence, in order to accomplish the mission goals within budget constraints, the computational tasks composing a workflow should be migrated from the local network to the cloud only when the former does not have enough computational resources to execute successfully the tasks (outbursting). In terms of outreach, this project will develop a pipeline of diverse and computer literate engineers who will be able to solve self-management CPS problems. The PIs will 1) create a course on real-time in-situ distributed computing (for graduate computer engineering and undergraduate non-engineering majors); 2) develop teaching modules for incorporation into key high-school activities; 3) leverage existing minority student outreach programs and networks at Rutgers; 4) incorporate exchange programs and team-teaching approaches; and 5) utilize distributed education technologies with application to robotics and networking. Our electrical/computer and mechanical engineering team has the theoretical and system-level skills, cross-disciplinary expertise, as well as a verifiable history of fruitful collaboration to exploit fully this project's research and educational potential.

Sub-millimeter scale cyber-physical systems will have a major impact on future applications. For example, targeted drug delivery or materials conveyance for micro-scale construction are two important application areas on which small-scale systems will advance the current state of the art. However, conventional actuator, sensor, and computational units are generally not available at extremely small scales. This project thus explores the relationships between novel microfabrication, system design for articulated locomotion, and active control of micro, cyber-physical systems. More specifically, this project develops a common analytical framework to understand, express, and reason about the connections, as well as demonstrate on a novel problem, the benefits of self-propelled articulated micro-scale devices. The project is developing elasto-magnetic filaments formed by linked ferromagnetic beads. These filaments can serve as the basis for functionalized structures, employing protein-coatings, that are flexible and controllable through actively manipulated distributions of magnetic dipole moments. This approach uses dual laser polymerization to construct templates that enable the magnetization profile of chains composed by single micron diameter ferromagnetic spheres, bonded by DNA origami strands, to be actively programmed. These elasto-magnetic bodies are then articulated by changes in an externally applied magnetic field, i.e., when subjected to a constant but oscillating weak magnetic field, the local alignment of dipole moments to the field will actively "actuate" the systems. The analysis, based on a geometric framework, will determine the optimal distribution of magnetization profiles across the filaments; thereby linking fabrication to analysis and the geometry underlying locomotion in dissipative fluids to novel maneuvering capabilities. Guided by this framework, as a demonstration, microrobots with these magnetized bodies will be designed to achieve specific locomotion objectives in sufficient numbers to be made to move purposefully in uncertain environments.

The confluence of two powerful global trends, (1) the rapid growth of cloud computing and data centers with skyrocketing energy consumption, and (2) the accelerating penetration of renewable energy sources, is creating both severe challenges and tremendous opportunities. The fast growing renewable generation puts forth great operational challenges since they will cause large, frequent, and random fluctuations in supply. Data centers, on the other hand, offer large flexible loads in the grid. Leveraging this flexibility, this project will develop fundamental theories and algorithms for sustainable data centers with a dual goal of improving data center energy efficiency and accelerating the integration of renewables in the grid via data center demand response (DR) and workload management. Specifically, the research findings will shed light on data center demand response while maintaining their performance, which will help data centers to decide how to participate in power market programs. Further, the success of data center demand response will help increase renewable energy integration and reduce the carbon footprint of data centers, contributing to global sustainability. The PIs will leverage fruitful collaboration to eventually bring the research to bear on ongoing industry standardization and development efforts. The PIs teach courses spanning networks, games, smart grid and optimization, and are strongly committed to promoting diversity by providing research opportunities to underrepresented students. Built on the PIs expertise on data centers and the smart grid, this project takes an interdisciplinary approach to develop fundamental theories and algorithms for sustainable data centers. The research tasks are organized under two well-coordinated thrusts, namely agile data center DR and adaptive workload management. The strategies and decisions of data center DR will be made based on the workload management algorithms that balance quality of service and energy efficiency and determine the supply functions. The workload management algorithms will optimize quality of service under the electric load constraints imposed by DR accordingly. This project will make three unique contributions: (1) new market programs with strategic participation of data centers in DR, instead of passive price takers, (2) fundamental understanding of the impacts of power network constraints on data center DR and new distributed algorithms for solving optimal power flow with stochastic renewable supplies, and (3) high-performance dynamic server provisioning and load balancing algorithms for large scale data centers under time-varying and stochastic electric load constraints and on-site renewable generation.