A cyberphysical system (CPS) in biology requires sensor input that represents, as closely as possible, cell activity. Much work is expended on the development of wearable sensors that detect the expression of cell activity filtered through many processes. Recent work discloses that gene transcription can be thought of as a signal, with periodic oscillations over time. The well-known 24 hour light-dark cycle has protean effects however shorter and longer cycles not only exist but have important roles to play in health and disease. Detection of these signals and their perturbation is likely to be of great use in a robust health focused CPS. The exact nature of these signals and the mathematical structure underlying them will form the basis of this proposal. The societal impacts go beyond the new sensors to include the development of open source methods allowing the dissemination of new mathematical models and insights. into measurement of cellular processes. This proposal addresses the critical problem of generating cell-level physiologic data as a substrate for an effective CPS in health. Applying new, unbiased signal processing techniques, the team has recently identified new periodicity in RNA over time. This signal provides a robust insight into cell function and its changes. The team will address the ability of the new techniques in specific situations to uncover signals to be used as inputs for a human health CPS sensor. This signal processing technique will be used to identify oscillations in genes associated with defined chronic metabolic diseases of humans such as diabetes, inflammation, and cancer). These candidate genes will be used to construct a precision signature for input into a CPS sensor. The concepts and data will be used to construct mathematical equations describing the longitudinal DNA transcripts previously identified. Taken together, these two activities will provide an integrated mathematical picture of periodic gene transcription that then sets the stage for novel sensor design that will provide prediction and control in a human-based CPS. The project will develop a new platform for understanding the cell that will be made widely available via a Web-based open source platform.

This project develops advanced cyber-physical sensing, modeling, control, and optimization methods to significantly improve the efficiency of algal biomass production using membrane bioreactor technologies for waste water processing and algal biofuel. Currently, many wastewater treatment plants are discharging treated wastewater containing significant amounts of nutrients, such as nitrogen, ammonium, and phosphate ions, directly into the water system, posing significant threats to the environment. Large-scale algae production represents one of the most promising and attractive solutions for simultaneous wastewater treatment and biofuel production. The critical bottleneck is low algae productivity and high biofuel production cost. The previous work of this research team has successfully developed an algae membrane bioreactor (A-MBR) technology for high-density algae production which doubles the productivity in an indoor bench-scale environment. The goal of this project is to explore advanced cyber-physical sensing, modeling, control, and optimization methods and co-design of the A-MBR system to bring the new algae production technology into the field. The specific goal is to increase the algal biomass productivity in current practice by three times in the field environment while minimizing land, capital, and operating costs. Specifically, the project will (1) adapt the A-MBR design to address unique new challenges for algae cultivation in field environments, (2) develop a multi-modality sensor network for real-time in-situ monitoring of key environmental variables for algae growth, (3) develop data-driven knowledge-based kinetic models for algae growth and automated methods for model calibration and verification using the real-time sensor network data, and (4) deploy the proposed CPS system and technologies in the field for performance evaluations and demonstrate its potentials. This project will demonstrate a new pathway toward green and sustainable algae cultivation and biofuel production using wastewater, addressing two important challenging issues faced by our nation and the world: wastewater treatment and renewable energy. It will provide unique and exciting opportunities for mentoring graduate students with interdisciplinary training opportunities, involving K-12 students, women and minority students. With web-based access and control, this project will convert the bench-scale and pilot scale algae cultivation systems into an exciting interactive online learning platform to educate undergraduate and high-school students about cyber-physical system design, process control, and renewable biofuel production.

A cyberphysical system (CPS) in biology requires sensor input that represents, as closely as possible, cell activity. Much work is expended on the development of wearable sensors that detect the expression of cell activity filtered through many processes. Recent work discloses that gene transcription can be thought of as a signal, with periodic oscillations over time. The well-known 24 hour light-dark cycle has protean effects however shorter and longer cycles not only exist but have important roles to play in health and disease. Detection of these signals and their perturbation is likely to be of great use in a robust health focused CPS. The exact nature of these signals and the mathematical structure underlying them will form the basis of this proposal. The societal impacts go beyond the new sensors to include the development of open source methods allowing the dissemination of new mathematical models and insights. into measurement of cellular processes. This proposal addresses the critical problem of generating cell-level physiologic data as a substrate for an effective CPS in health. Applying new, unbiased signal processing techniques, the team has recently identified new periodicity in RNA over time. This signal provides a robust insight into cell function and its changes. The team will address the ability of the new techniques in specific situations to uncover signals to be used as inputs for a human health CPS sensor. This signal processing technique will be used to identify oscillations in genes associated with defined chronic metabolic diseases of humans such as diabetes, inflammation, and cancer). These candidate genes will be used to construct a precision signature for input into a CPS sensor. The concepts and data will be used to construct mathematical equations describing the longitudinal DNA transcripts previously identified. Taken together, these two activities will provide an integrated mathematical picture of periodic gene transcription that then sets the stage for novel sensor design that will provide prediction and control in a human-based CPS. The project will develop a new platform for understanding the cell that will be made widely available via a Web-based open source platform.

Inadequate system understanding and inadequate situational awareness have caused large-scale power outages in the past. With the increased reliance on variable energy supply sources, system understanding and situational awareness of a complex energy system become more challenging. This project leverages the power of big data analytics to directly improve system understanding and situational awareness. The research provides the methodology for detecting anomalous events in real-time, and therefore allow control centers to take appropriate control actions before minor events develop into major blackouts. The significance for the society and for the power industry is profound. Energy providers will be able to prevent large-scale power outages and reduce revenue losses, and customers will benefit from reliable energy delivery with service guarantees. Students, including women and underrepresented groups, will be trained for the future workforce in this area. The project includes four major thrusts: 1) real-time anomaly detection from measurement data; 2) real-time event diagnosis and interpretation of changes in the state of the network; 3) real-time optimal control of the power grid; 4) scientific foundations underpinning cyber-physical systems. The major outcome of this project is practical solutions to event or fault detection and diagnosis in the power grid, as well as prediction and prevention of large-scale power outages.

The increasing set of functionalities, network interoperability, and system design complexity have introduced security vulnerabilities in cyber-physical systems (CPS). As recently demonstrated, a remote attacker can disrupt the operation of a car to either disable the vehicle or hijack it. High-profile security incidents in other CPS domains include a large-scale attack on Ukraine's power-grid and the StuxNet attack on an industrial system, while the RQ-170 Sentinel drone capture has shown that even safety-critical military CPS can be compromised. The tight integration of information technology and physical components has made CPS vulnerable to attack vectors well beyond the standard cyber-attacks. In addition, deep component embedding and long projected system lifetime limit the use of standard cyber security solutions that impose a significant computation and communication overhead. On the other hand, the safety-critical interaction with the physical world has made attacks on CPS extremely dangerous as they could result in significant physical damage and even loss of life. To address these challenges, this project will develop scientific foundations for design of secure control of CPS, resulting in a high-assurance CPS design framework in which a mix of attack-resilient control, security-aware human-CPS interactions, efficient controller instrumentation and system recovery provides safety and performance guarantees even in the presence of attacks. The goal of this project is to provide fundamentally new methods for security-aware modeling, analysis and design of safety-critical CPS, addressing the many different physical, functional and logical aspects of these heterogeneous systems in the presence of attacks. Specific research products include: 1) Cyber-physical security techniques that exploit the interaction between physical and cyber domains for attack-detection and resilient control; 2) Framework for secure control of Human-CPS that harnesses the human power of inductive reasoning and the ability to provide context, particularly during an attack, to improve the overall security guarantees; 3) Platform support for implementation of secure CPS controllers including design techniques and tools ensuring safe and efficient closed-loop recovery. Proposed high-assurance design framework will be used to develop security-aware automotive controllers for connected and autonomous vehicles with varying levels of autonomy and human supervision. Various components of the proposed research will be directly evaluated on relevant automotive applications and architectures, which will facilitate their transition into practice and immediate industrial impact. Furthermore, the general nature of the design framework provides a direct path for this research to have significant impact in other CPS domains leading to design of secure and safety-preserving CPS. The project also has an extensive education and outreach component, including curriculum development for high-assurance CPS with a strong systems and multidisciplinary perspective, expansion of hands-on research opportunities for undergraduate and graduate students, and cooperation with industry. These efforts are strongly motivated by industrial need to provide high-assurance for safety-critical CPS, and thus the results of this project will directly impact the way these systems are designed as well as education of the next generation workforce necessary to support evolution of safe and secure CPS.

This CAREER project aims to translate the smart electronic textile (e-textile) technologies to fill the need for telemedicine. For example, there are around 10 million individuals across the globe who live with Parkinson's disease. They are majorly elderly patients who experience tremors, rigidity, slowness of movement, and difficulties in walking and driving. Despite their disabling condition, they need to make periodic visits to clinics for progressive checks and interventions. The project specifically targets improving treatments for patients with deep brain stimulation (DBS) implants because specialized neurologists find DBS treatments very complex and time consuming. It is very important for the doctors to personalize the electrical stimulations for each and every patient. The proposed research establishes an intelligent infrastructure in patients' homes so that patients, without visiting clinics, could independently perform a medical screening consisting of movement exercises such as finger tapping, hand flipping, toe tapping, and foot stomping. Moreover, the system builds upon modern internet-of-things technologies to keep neurologists in the loop to execute telemedicine interventions. The project will result into an in-home, wearable internet-of-things hub (referred to as "IoT-Hub") consisting of smart e-textiles such as e-gloves, e-socks, and companion computing devices for data analytics, storage, and communications to the cloud servers of hospitals. IoT Hub will have unique features to translate the medical care from hospitals to homes: 1) the measurements of movement symptoms with a medical-grade precision through sensors woven into e-gloves and e-socks, 2) the patient-friendly interface driven by intelligent close-loop algorithms running on tablet computers, and 3) clinical data analytics for neurologists to make data-driven informed decision on DBS treatments. The proposed IoT-Hub closes the knowledge gap between the areas of IoT, healthcare, smart textiles, and patient-centered design - all of which require interdisciplinary scientific discoveries and innovations. This project will have a potentially profound impact on the growing number of elderly patients living with Parkinson's disease or other neurological conditions. Intelligent algorithms for wearable embedded systems, e-textile designs and circuits, and de-identified telemedicine data will be broadly disseminated to the scientific community. The PI has developed a unique IoT curriculum at URI that involves a series of design-centered activities; an IoT hack-a-thon for entrepreneurial thinking; and awearable IoT course for university students to learn innovating personalized health and wellness solutions; and wearable engineering program to engage high school students in hands-on engineering activities.

Pressing environmental problems, energy supply security issues, and nuclear power safety concerns drive the worldwide interest in renewable energy. The US Clean Energy Challenge calls for a partnership of states and communities to expand solar to 140GW by 2020. Investment in renewables today is in utility-scale solar plants and wind farms, as well as small-scale distributed rooftop photovoltaics (PV). Large solar plants are cheaper than rooftop PV, but this advantage is diminished when considering transmission infrastructure costs. Generous tax credits and net metering subsidies are responsible for much of the dramatic growth of distributed PV. Under net metering, utilities are mandated to buy back excess generation at retail prices. But tax credits are being phased out, and utilities strenuously oppose net metering policies as they allow PV owners to avoid paying for infrastructure costs and pose an existential threat to utility business models. The growth of distributed PV generation may decelerate. This project aims to sustain and accelerate future growth in distributed PV investments by enabling connected communities to share electricity services. The central thesis is that shared PV ownership and operations can spur greater investment in distributed PV with minimal subsidy, without net metering, and with participants fairly paying for infrastructure, reserves and reliability costs. Our research will enable connected communities to efficiently use resources, reduce emissions, and support our collective sustainability goals. It will spur deeper penetration of distributed PV without subsidy, while defining new entrepreneurial opportunities in the sharing economy for electricity services. The project will integrate education and research through new interdisciplinary courses that combine technology, economics, policy, and power systems. This research is broadly applicable to other shared services including electricity storage, building energy management, and transportation networks. Specifically, we will (a) develop the infrastructure necessary for sharing electricity services, (b) analyze investment decisions of households under various tariff and subsidy designs, (c) construct behavioral model that predict consumer response to incentives, and (d) conduct an empirical assessment of sharing grounded in data. In our architectural vision for sharing, agents interact with each other through a cloud based supervisory system. This system manages constraints, accepts supply and demand bids for shared resources, clears the market, and publishes prices. A key element of our architecture is software-define-power-flow to scale sharing to millions of clients under a peer-to-peer matching platform. We will make the business case for sharing in the energy sector using game-theoretic methods and micro-economic tools to analyze investment decisions in a sharing economy for electricity services. Recruiting clients to share their resources is a key research challenge. Here, we will apply modern machine learning methods to identify, model, and target suitable clients. Finally, we will use data analytics methods to make a compelling case for sharing based on city-scale data.

Ocean Big Data (OBD) is an emerging area of research that benefits ocean environmental monitoring, offshore exploration, disaster prevention, and military surveillance. It is now affordable for oil and gas companies, fishing industry, militaries, and marine researchers to deploy physical undersea sensor systems to obtain strategic advantages. However, these sensing activities are scattered, isolated, and often follow the traditional "deploy, wait, retrieve, and post-process" routine. Since transmitting information underwater remains difficult and unreliable, these sensors lack a cyber interconnection, which severely limits ocean cyber-physical systems. This project aims to providing a viable cyber interconnection scheme that enables distributed, efficient, ubiquitous, and secure (DEUS) data delivery from underwater sensors to the surface station. The proposed cyber interconnection scheme features cheap underwater sensor nodes with energy harvesting capability, a fleet of autonomous underwater vehicles (AUVs) for information ferrying, advanced magnetic-induction (MI) antenna design using ferrite material, distributed algorithms for efficient data collection via AUVs, and secure data delivery protocols. The success of this project will help push the frontier of Internet of Things in Oceans (IoTO) and OBD, both of which will find numerous underwater applications in offshore oil spill response, fisheries management, storm preparedness, etc., which impact the economy and well-being of not only coastal regions but also inland states. The project will also provide special interdisciplinary training opportunities for both graduate and undergraduate students, particularly women and minority students, through both research work and related courses on underwater wireless communication, network security, and AUV designs. The DEUS project provides a viable cyber interconnection scheme that enables distributed, efficient, ubiquitous, and secure data delivery in underwater environment via four synergistic thrusts: (1) integration of underwater wireless sensor and communication systems, which will enhance the current MI and light communication means of underwater sensors, integrate acoustic transmission systems for long-range communications between anchor nodes and AUVs, and design energy harvesting and replenishment solutions to prolong the lifetime of underwater sensors (30+ years); (2) distributed and ubiquitous data delivery via multiple AUVs, which aims to collect the distributed data and deliver them ubiquitously throughout the underwater network by employing ferrite material and triaxial induction antennas and mounting them outside of the AUV body for MI enhancement, and developing algorithms of multiple AUVs' path-planning, trajectory optimization, etc. under dynamic network conditions; (3) efficiency and security in data delivery, which designs network algorithms to improve the efficiency and security of data delivery. Instead of collecting data from every sensor via acoustic communications, the AUVs choose some sensors to collect data with the high data rate transmission mode in near field (e.g., light), and allowing the sensor far away from the AUVs to send its data either directly to AUVs via acoustic wave or to its nearby chosen sensors via MI/light communications. A secure data delivery scheme will also be developed to not only secure the data delivery against typical malicious attacks and guarantee the integrity of collected data, but also allow the data aggregation of one business entity without knowing others' private business information; (4) experimental validation and testing, which will verify the proposed data delivery schemes, and quantitatively present the performance gains through simulations, experiments and field test, based on existing facilities.

Magnetic Resonance Imaging (MRI) scanners use strong magnetic fields to safely image soft tissues deep inside the body. They offer a unique tool for guiding therapies: images while patient is inside the scanner can localize diseased tissue and guide an intervention with high accuracy. This research controls MRI magnetic fields to wirelessly push millimeter-scale robots through vessels in the body, assemble them into tools, and provide targeted drug delivery or pierce tissue. This will directly impact healthcare, improving patient outcome by enabling unparalleled minimal invasiveness resulting in faster recovery, fewer side effects, and cost-effectiveness. This transformative toolset for multi-agent control will set the foundation for a wealth of medical therapies and surgical interventions. Using magnetic forces of clinical MRI scanners to steer miniature tetherless effectors through human bodies and combining with real-time imaging and operator immersion could transform the practice of minimally invasive interventions. This CPS will seamlessly integrate physical (scanner sensor/actuator, effectors, patient, operator) and cyber (world modeling, combined sensor and effector control, operator immersion). Work entails: (1) Portfolio of parametric effector designs that can be optimized to exploit the constraints of a given clinical procedure. (2) Toolbox of automatic controllers for MRI-based powering and steering of tetherless effectors in the body lumen, self-assembling them into tools, and precision therapy delivery or to pierce tissue. (3) Real-time MRI-based sensing of the physical world for imaging and tracking effectors and tissue. (4) Linked effector and MRI scanner control on-the-fly. (5) Visual/force-feedback human-robot interfacing. The work focuses on two effector classes: an MRI Gauss gun that stores magnetic potential energy released by a chain reaction when robots self-assemble, and an MRI pile-driver that converts kinetic energy from an enclosed sphere into impulses to tunnel into tissue. These approaches will be validated through analytical modeling, scaled hardware experiments, and experiments in clinical MRI scanners.

To reduce reliance on other countries for minerals (e.g., coal, rare-earth metals), the USA has seen an invigoration of mining activity in recent years. Unfortunately, miners often have to work in dangerous environments where there is risk of mine explosions, fires, poisonous gases, and flooding in tunnels. Mine accidents have killed over 500 US and 40,000 mine workers worldwide in the past decade. Most of these accidents occurred in structurally diverse underground mines with extensive labyrinths of interconnected tunnels, where the environment continually changes as mining progresses and machinery is repositioned, complicating search and rescue efforts. In recognition of the severity of the problem, the Mine Improvement and New Emergency Response Act passed in 2006 mandated mines to monitor levels of methane, carbon monoxide, smoke, and oxygen to warn miners of possible danger due to air poisoning, fire, or explosions. The Act also mandated plans to rapidly and safely respond in post-accident scenarios, involving two-way, wired or semi-wired tracking and communication systems that could save lives during entrapment and water inundation emergencies. But the high cost of deploying such a safety infrastructure encourages companies today to meet only the bare minimum required safeguards. This project will involve transformative, foundational, and synergistic research that is necessary to overcome monitoring, communication, and tracking challenges in the underground mining context, to realize a cost-effective safety infrastructure that can be deployed in any type of underground mine. Such a framework will not only minimize the risks facing hundreds of thousands of miners in the USA today, but the foundational research outcomes will also be applicable to a wide range of applications in the realms of Smart and Connected Communities (S&CC) and Internet of Things (IoT), wherever the emphasis is on creating smart workplaces, sustainably operating in harsh environments, and improving human safety. The principal objective of this proposal is to devise, design, prototype, and test a fundamentally novel wireless cyber-physical framework of low-cost, energy-efficient, and reliable sensor nodes and commodity smartphones for monitoring, tracking, and communication, to improve miner safety in underground mines. This synergy project contributes to the science and engineering principles needed to realize Cyber-Physical Systems and seeks to grow at the intersection of three research thrusts: quality-aware voice and data streaming, mobile computing assisted location tracking, and computational electromagnetics driven wireless signal characterization. These three thrusts (1) introduce novel mechanisms to enable the co-existence of high quality voice streams with environmental sensor data streams in low-power wireless mesh networks of sensor nodes operating in noisy underground environments; (2) develop schemes for energy-efficient scheduling of location queries and error-tolerant indoor localization to locate individual miners and groups of miners underground; and (3) characterize wireless signal behavior with electromagnetic modeling in highly complex and uncertain environments, based on measurements from a real underground mine, to guide optimal placement of wireless nodes in mining tunnels. Not only is the convergence of these thrusts novel as a whole, but also the techniques and insights developed for each thrust are transformative and go beyond conventional approaches. Collaboration with a mining company for technology transfer will enable rapid real-world deployment of the proposed research. The broader impacts of the research will tightly integrate research results into all levels of teaching, including graduate, undergraduate, and K-12 education; broaden the participation of women and minority students in Cyber-Physical research; and integrate research into the syllabi of existing and new courses.