Cyber-Physical Systems (CPS) offer the promise for radical changes to our everyday life by enabling the physical world to be programmed in the same way that a computer is programmed. The physical world, however, is far less predictable than a computer and this renders the design of CPS very challenging. In order to reduce the impact of unforeseen events arising from the physical world, or even from the cyber world, this project develops a science of CPS robustness. A robust CPS will only modestly deviate from its desired behavior upon the occurrence of unforeseen circumstances and has the ability to recover once these disrupting circumstances subside. The intellectual merit of this project is the development of a science of CPS robustness that harnesses the intricate interactions between cyber and physical components to obtain CPS that are able to operate in a wide range of unpredictable environments. The project?s broader significance and importance is the enablement of vast number of applications requiring CPS to operate seamlessly in unpredictable environments such as the internet-of-things or smart and connected communities. At the technical level, this project leverages existing notions of robustness for cyber systems, such as self-stabilizing algorithms, and for physical systems, such as input-to-state stability, to create a science of CPS robustness. Expected outcomes include new temporal logics to specify CPS robustness, verification and synthesis algorithms for CPS robustness, as well as compositional design flows.

This research investigates a cyber-physical framework for scalable, long-term monitoring and maintenance of civil infrastructures. With growth of the world economy and its population, there has been an ever increasing dependency on larger and more complex networks of civil infrastructure as evident in the billions of dollars spent by the federal, state and local governments to either upgrade or repair transportation systems or utilities. Despite these large expenditures, the nation continues to suffer staggering consequences from infrastructural decay. Therefore, paramount to the concept of a smart city of the future is the concept of smart civil infrastructure that can self-monitor itself to predict any impending failures and in the cases of extreme events (e.g. earthquakes) identify portions that would require immediate repair, and prioritize areas for emergency response. A goal of this research project is to make significant progress towards this grand vision by investigating a framework of infrastructural Internet-of-Things (i-IoT) using a network of self-powered, embedded health monitoring sensors. The collaborative and interdisciplinary nature of this research would provide opportunities for unique outreach programs involving undergraduate and graduate students in technical areas, e.g., sensors, IoTs and structural health monitoring. The project would also provide avenues for disseminating the results of this research to stakeholders in the state governments and for translating the results of the research into field deployable prototypes. This research addresses different elements of the proposed i-IoT framework by bringing together expertise from three universities in the area of self-powered sensors, energy scavenging processors, structural health monitoring and earthquake engineering. At the fundamental level, the project involves investigating self-powered sensors that will require zero maintenance and can continuously operate over the useful lifespan of the structure without experiencing any downtime. The challenge in this regard is that sensors need to occupy a small enough volume such that an array of these devices could be easily embedded and can provide accurate spatial resolution in structural imaging. This research is also investigates techniques that would enable real time wireless collection of data from an array of self-powered sensors embedded inside a structure, without taking the structure out-of-service. The methods to be explored involve combining the physics of energy scavenging, transduction, rectification and logic computation to improve the system's energy-efficiency and reduce the system latency. At the algorithmic level the project explores novel structural failure prediction and structural forensic algorithms based on historical data collected from self-powered sensors embedded at different spatial locations. This includes kernel algorithms that can exploit the data to quickly identify the most vulnerable part of a structure after a man-made or a natural crisis (for example an earthquake). Finally, the technology translation plan for this research is to validate the proposed i-IoT framework in real-world deployment, which includes buildings, multi-span bridges and highways.

Modern ground vehicles are complex cyber-physical systems (CPS) in which many functions are achieved by collaborative interactions between mechanical systems and electronic control units. In addition, human drivers also play important roles on the vehicle driving. For such cyber-human-vehicle systems (CHVS), the synergistic collaborations and integrations among human drivers, vehicle active motion control, and onboard real-time computation and communication are critical for enhancing vehicle driving safety. With the recent advances on vehicle onboard computation and communication technologies, this project aims to develop onboard-adaptable and personalizable human driver models, create driver-specific vehicle active motion control systems, design dynamic onboard real-time computation task scheduling methods that can effectively synthesize with the personalized vehicle motion control methods, and integrate the vehicle-to-vehicle communications for driver-specific, inter-vehicle motion control. The research, upon successful completion, can create methodologies for optimally synthesizing onboard computation and communications, individual human driving characteristics, and vehicle dynamics and active motion control to form a novel CHVS that can enhance the driving safety and increase the likelihood of collision-avoidance. The research objectives will be pursued through analytical, computational, and experimental studies. Driving simulator and real vehicle experiments together with high-fidelity simulations will assist the investigations. The research results from this project will be disseminated through usual academic publications, CPS meetings, and visits to relevant companies for industrial collaborations. Some of the research findings will be used to enrich several undergraduate and graduate courses in different disciplines. High-school summer camp and undergraduate student research opportunities will be generated through this project to attract students to engage in the research and to pursue higher education in science and engineering.

This project will analyze how smart and pervasive devices including human and vehicle-borne sensors can be harnessed to effectively map and identify urban heat islands (UHIs), and mitigate UHI associated risks on various communities. Excessive generation and retention of heat in urban areas by the built environment results in UHIs. Driven by climate change, extreme heat events are increasingly posing a major health hazard to many urban communities in U.S. and around the world. Studies analyzing the impact of UHIs on communities have primarily focused on generating coarse grained heat maps of cities using satellite or weather station data, and correlating heat events with human mortality and morbidity data. This exploratory project will develop and test a prototype community-centric approach to urban heat vulnerability research. Focusing on heat stress risks of individuals and communities in fine-granular geographical areas will radically transform UHI research and efforts to mitigate them. The findings from this study will be extremely useful for understanding the heat exposure vulnerabilities of individual communities such as people living in poorly-planned neighborhoods, poor and elderly, city and municipal outdoor workers, construction workers, bus commuters, and mail delivery personnel. Furthermore, this study will lay the foundation for city/local government officials and business leaders to devise targeted and more efficacious heat hazard mitigation efforts such as increasing greenspace and developing better heat-safety policies for their workers. This research will build a scalable and robust smart-sensor-cloud framework for leveraging variety of human and vehicle-borne smart sensors (e.g., smartphones, environmental micro data loggers) in conjunction with traditional data sources (e.g., satellites and weather stations) for gathering, and analyzing accurate and fine-grained temperature information for urban areas as well as specific urban communities. In this context several important questions will be addressed including: (1) How to effectively harness and integrate heterogeneous data from multiple devices such as smartphones, Unmanned Aerial System (UAS) sensors, micro data loggers, and other modern sensing technologies to create UHI maps for individuals and communities? (2) What are the spatial and temporal differences and variability between satellite, UAS and smart-device derived UHI maps, and what is the optimum granularity required to develop a standardized UHI mapping protocol? and (3) What are the differences in heat exposure levels within a community based on socio-economic factors such as demographics, occupation, and residence location? The temperature maps will be generated using multiple smart devices such as UAS mounted thermal sensors, micro temperature sensors (e.g., Kestrel drops), and iPhone and Android mobile phone based applications. Various field experiments and simulations will be performed to develop temperature conversion calibration coefficients in order to enhance the accuracy of the maps. The temperature maps will be compared with coincident UAS and satellite derived heat maps to analyze the loss of spatial variability of UHIs within an urban area. This project will expand beyond the limits of conventional UHI research by developing hyperlocal and community-centric heat hazard models which will allow the assessment of a community's or an individual's heat stress risk, a tangible step toward a personalized heat warning system.

Improvements to the urban physical landscape, such as adding new greenspaces and healthy activity destinations, or removing problem areas like "blight" are decided upon using ideas, plans and existing theory. Yet little is ever done to evaluate the effectiveness of such improvements. The project develops a visual analytics platform of anonymized human mobility and human opinion data retrieved from social media, so that community-focused stakeholders can interactively study human activity usage and associate insights around multiple location types. In this way questions can be asked such as has this change actually done any good on this/my street? Or, where can I tell my patient to exercise that is safe, culturally acceptable, and appropriate to who he/she is? This visual analytics system can become part of a best practices approach when re-designing urban environments, where re-designing means changing aspects of the built, social and physical environment. The Broader Impacts of this work include potential usage beyond academia; a doctor interactively investigating the neighborhood around a patient, or a community group considering the likely impact of adding a community garden by investigating the changes such an initiative has had elsewhere. The project provides a non-specialist software interface that makes that type of analysis ubiquitous. This tool can be applied irrespective of cultural, racial or economic barriers. The geographic area of the proposed study comprises largely of minority neighborhoods allowing us to show the utility of this approach to attack issues of racial disparity. Every effort will be made to attract and employ minority students on the project in order to reflect the social makeup of these study neighborhoods. This project is the first near real time intervention tool designed for community use that utilizes the most dynamic data available: social media trajectory data and associated contextual meaning. The technique takes social media and leverages it in an interactive visual system to answer day-to-day questions by non-specialized users. It captures behaviors/activities related to urban communities by investigating and understanding social media trajectory data. These points of investigation will be gleaned from a rich array of sources including community narratives. Important negative and positive spaces, and changes to those spaces, will be investigated to justify the utilization of big social media data and evaluate our visual analytics system. A database, NeighborBase, manages these heterogeneous data extracted from tweets and support various real-time queries. A community-scale visual analytics tool, NeighborVis, further provides intuitive interactions for users to perform efficient knowledge discovery over NeighborBase. The software platform seamless integrates these data with computational and visualization algorithms for the design and improvement in physical communities.

Acoustic sensing-based preventive maintenance approach focuses on mapping auditory information, captured from mechanical systems in buildings, to their health status and probability of impending failures. An important application of this methodology is reducing energy waste in commercial heating, ventilating, and air-conditioning (HVAC) systems, which accounts for over 42% of the total U.S. commercial building energy usage. The outcome of this project is a robust acoustic sensing technology that has a high accuracy in predicting actual failures of HVAC systems. This research will be integrated with new user interfaces that will allow building managers to virtually navigate the equipment and appliances in large buildings (or collections of buildings), and to quickly identify potential failures. This EArly-Concept Grants for Exploratory Research (EAGER) project addresses the following technology gaps as it translates from research discovery toward commercial applications: (a) ensuring privacy, and (b) minimizing false positives in predicting equipment failure. This project develops acoustic signal acquisition and processing techniques that preserve the privacy of everyone and everything that is susceptible to privacy violations due to continuous acoustic monitoring. The proposed collaborative research enables buildings to be retrofitted with a low-cost, acoustic sensing solution to monitor its HVAC systems to predict their impending failures. A major goal of this project is to reduce false positives when making these predictions that are primarily caused by inadequate modeling of sounds from a faulty component, inadequate modeling of different types of faults, and errors in sound source recognition. Furthermore, this project creates a foundation for the next generation of intelligent systems that autonomously monitor equipment and predict failure. The project engages University of Florida and University of North Carolina at Chapel Hill to augment research capability in conducting visualization-based dynamic assessment of HVAC systems, and building low-cost, embedded device-based centralized HVAC monitoring systems. With a cloud-connected network of embedded audio monitoring devices deployed in the University of Florida campus buildings for running acoustic processing and classification tasks, this novel and transformative technology is aimed at identifying and solving challenges in large-scale, commercial-grade deployment of such systems in real world scenarios. This project will engage an industrial partner to develop privacy-preserving algorithms, build test environments, and guide commercialization aspects of this technology.

Heavy vehicles, such as trucks and buses, are part of the US critical infrastructure and carry out a significant portion of commercial and private business operations. Little effort has been invested in cyber security for these assets. If an adversary gains access to the vehicle's Controller Area Network (CAN), attacks can be launched that can affect critical vehicle electronic components. Traditionally, physical access to a heavy vehicle was required to access the CAN. However, wireless devices are also installed on heavy vehicles, which open trucks and busses to remote wireless cyber attacks. This project explores cyber security vulnerabilities related to wireless devices that communicate on the CAN. For identified threats, researchers determine the proper mitigation strategies, including where and how they are best deployed. To demonstrate potential exploits and subsequent trust in proposed mitigation strategies, this project designs and implements a scalable, high-fidelity test bed using actual heavy vehicle electronic control units, such as engine and brake controllers. The test bed includes built-in mechanisms for remote access and secure information delivery to allow for collaboration among researchers at different sites. The results of the research, including the potential to extend the test bed with other components, can impact cyber security analysis for other industries that use CAN, such as building automation, medical devices, and manufacturing. The SAE J1939 communication network in heavy vehicles is based on CAN and has open documentation for packet definition and transmission. This openness may be exploited for creating spoofed J1939 messages. Heavy vehicle owners utilize third-party systems, such as remote telematics, that introduce new J1939 enabled modules, which can potentially be subverted by an adversary. This project uses these systems to gain remote access and attack another CAN connected electronic control unit. Packet sniffing is performed as the telematics system connects wirelessly to the CAN to determine if fake packets can be inserted. Research includes examining different designs, configurations, and deployments of intrusion detection systems to best thwart such remote attacks using the developed test bed. One challenge is to develop algorithms that can act in real-time with deployed test bed hardware. Research includes developing scientific strategies to measure the temporal response of the cyber actions in the test bed and the reaction time of any intrusion detection system, so that bounds can be determined based on the ability to conduct a remote cyber operation on a J1939 network.

The Sensors in a Shoebox project focuses on empowering urban citizens with the tools and methods necessary to observe and analyze the physical, social, and natural systems that affect their communities for improved community-based decision making. The project creates an affordable and ruggedized sensor kit that consists of solar-powered wireless sensors with Internet connectivity that can be distributed to communities to sense environmental parameters, vibrations, motion, among other parameters. Data is transmitted from community-deployed sensor kits to the cloud where sensor data is stored and managed. The community directly accesses their data from a web portal offering a suite of user-friendly analytical tools that citizens could use to extract community-relevant information from raw sensor data. Some envisioned community uses of the Sensors in a Shoebox platform include but not limited to: measuring neighborhood air quality, tracking the usage of public spaces, and observing residents' mobility choices (walking, biking, and motorized transport). This project will provide a scientific and technological foundation to the extension of cyber-physical systems to explicitly include humans. So called cyber-physical-social systems, these human-in-the-loop systems have the potential to transform a variety of commercial application including those in transportation, building energy management, among others. The project engages the communities of Detroit, a city beginning to go through transformation after decades of dramatic population declines. Specifically, the project recruits middle- and high school students from Detroit public charter schools to serve at the front lines of the system design and deployment. In doing so, the team will closely study and rigorously assess the experiences of urban youth using the system. In particular, advancement of STEM knowledge and youth's notions of being connected citizens will be qualitatively and quantitatively assessed.

This NSF Cyber-Physical Systems (CPS) Frontier project "Verified Human Interfaces, Control, and Learning for Semi-Autonomous Systems (VeHICaL)" is developing the foundations of verified co-design of interfaces and control for human cyber-physical systems (h-CPS) --- cyber-physical systems that operate in concert with human operators. VeHICaL aims to bring a formal approach to designing both interfaces and control for h-CPS, with provable guarantees. The VeHICaL project is grounded in a novel problem formulation that elucidates the unique requirements on h-CPS including not only traditional correctness properties on autonomous controllers but also quantitative requirements on the logic governing switching or sharing of control between human operator and autonomous controller, the user interface, privacy properties, etc. The project is making contributions along four thrusts: (1) formalisms for modeling h-CPS; (2) computational techniques for learning, verification, and control of h-CPS; (3) design and validation of sensor and human-machine interfaces, and (4) empirical evaluation in the domain of semi-autonomous vehicles. The VeHICaL approach is bringing a conceptual shift of focus away from separately addressing the design of control systems and human-machine interaction and towards the joint co-design of human interfaces and control using common modeling formalisms and requirements on the entire system. This co-design approach is making novel intellectual contributions to the areas of formal methods, control theory, sensing and perception, cognitive science, and human-machine interfaces. Cyber-physical systems deployed in societal-scale applications almost always interact with humans. The foundational work being pursued in the VeHICaL project is being validated in two application domains: semi-autonomous ground vehicles that interact with human drivers, and semi-autonomous aerial vehicles (drones) that interact with human operators. A principled approach to h-CPS design --- one that obtains provable guarantees on system behavior with humans in the loop --- can have an enormous positive impact on the emerging national ``smart'' infrastructure. In addition, this project is pursuing a substantial educational and outreach program including: (i) integrating research into undergraduate and graduate coursework, especially capstone projects; (ii) extensive online course content leveraging existing work by the PIs; (iii) a strong undergraduate research program, and (iv) outreach and summer programs for school children with a focus on reaching under-represented groups.

Software-Defined Control (SDC) is a revolutionary methodology for controlling manufacturing systems that uses a global view of the entire manufacturing system, including all of the physical components (machines, robots, and parts to be processed) as well as the cyber components (logic controllers, RFID readers, and networks). As manufacturing systems become more complex and more connected, they become more susceptible to small faults that could cascade into major failures or even cyber-attacks that enter the plant, such as, through the internet. In this project, models of both the cyber and physical components will be used to predict the expected behavior of the manufacturing system. Since the components of the manufacturing system are tightly coupled in both time and space, such a temporal-physical coupling, together with high-fidelity models of the system, allows any fault or attack that changes the behavior of the system to be detected and classified. Once detected and identified, the system will compute new routes for the physical parts through the plant, thus avoiding the affected locations. These new routes will be directly downloaded to the low-level controllers that communicate with the machines and robots, and will keep production operating (albeit at a reduced level), even in the face of an otherwise catastrophic fault. These algorithms will be inspired by the successful approach of Software-Defined Networking. Anomaly detection methods will be developed that can ascertain the difference between the expected (modeled) behavior of the system and the observed behavior (from sensors). Anomalies will be detected both at short time-scales, using high-fidelity models, and longer time-scales, using machine learning and statistical-based methods. The detection and classification of anomalies, whether they be random faults or cyber-attacks, will represent a significant contribution, and enable the re-programming of the control systems (through re-routing the parts) to continue production. The manufacturing industry represents a significant fraction of the US GDP, and each manufacturing plant represents a large capital investment. The ability to keep these plants running in the face of inevitable faults and even malicious attacks can improve productivity -- keeping costs low for both manufacturers and consumers. Importantly, these same algorithms can be used to redefine the production routes (and machine programs) when a new part is introduced, or the desired production volume is changed, to maximize profitability for the manufacturing operation.