The nation's critical infrastructures are increasingly dependent on systems that use computers to control vital physical components, including water supplies, the electric grid, airline systems, and medical devices. These are all examples of Cyber-Physical Systems (CPS) that are vulnerable to attack through their computer systems, through their physical properties such as power flow, water flow, chemistry, etc., or through both. The potential consequences of such compromised systems include financial disaster, civil disorder, even the loss of life. The proposed work significantly advances the science of protecting CPS by ensuring that the systems "do what they are supposed to do" despite an attacker trying to make them fail or do harm. In this convergent approach, the key is to tell the CPS how it is supposed to behave and build in defenses that make sure each component behaves and works well with others. The proposed work has a clear transition to industrial practice. It will also enhance education and opportunity by opening up securing society as a fascinating discipline for K-12 students to follow. The objective of the proposed project is to produce, from untrusted components, a trusted Cyber-physical system (CPS) that is resilient to security attacks and failures. The approach will rely on information flows in both the cyber and physical subsystems, and will be validated experimentally on high fidelity water treatment and electric power CPS testbeds. The project brings together concepts from distributed computing, control theory, machine learning, and estimation theory to synthesize a complete mitigation of the security and operational threats to a CPS. The proposed method's key difference from current methods is that security holes will be identified and plugged automatically at system design time, then enforced during runtime without relying solely on secure boundaries or firewalls. The system will feature the ability to identify and isolate a malfunctioning device or cyber-physical intrusion in real-time by validating its operation against fundamental scientific/engineering principles and learned behavior. A combined mathematical/data science approach will be used to generate governing invariants that are enforced at system runtime. Invariants are a scientific approach grounded in the system's physics coupled with machine learning and real-time scheduling approaches embedded in the CPS. Robust state estimation will account for errors in measurement and automated security domain construction and optimization to reduce the cost of evaluation without sacrificing coverage. The successful outcome of this research will lead to improved national security across various CPS infrastructures which, in turn, will improve economic and population health and security. The work can be taken to industry for deployment in critical infrastructures. The project will stimulate interest in Science, Technology, Engineering and Mathematics (STEM) through the development of a water-themed tabletop exercise for K-12 and helping current college students develop an interest in outreach through the experiential learning aspects of developing the tabletop exercise.

The push towards deploying autonomous-driving capabilities in vehicles is happening at breakneck speed. Semi-autonomous features are becoming increasingly common, and fully autonomous vehicles at mass-market scales are on the horizon. Cameras are cost-effective sensors, so computer-vision techniques have loomed large in implementing autonomous features. In a vehicle, these techniques must function "in real time." Unfortunately, this requirement lies at the heart of a significant disconnect: when computer-vision researchers refer to "real time," they usually mean "real fast"; in contrast, certifiable automotive systems must be "real time" in the sense of being able to predictably react to input information (such as a detected pedestrian) within specified deadlines so that adverse outcomes (such as striking a pedestrian) are provably precluded. The goal of this project is to eliminate this disconnect. It will do so through research on several fronts. First, a real-time computer-vision programming framework will be created by extending OpenVX, which is a recently ratified standard intended for developing computer-vision applications for embedded systems. Second, new computer-vision algorithms that exploit the features of this programming framework will be created, and methods will be developed to transform existing algorithms to make them "real time" in a predictability sense. Third, an experimental evaluation of "real-fast" vs. "real-time" computer vision will be conducted using driving simulators, sub-scale autonomous vehicles, and advanced testing infrastructure at General Motors. While industry is pushing hard in the area of autonomous driving, autonomous vehicles will never become a common mode of transportation unless methods for certifying real-time safety are produced. This project will focus on a key aspect of certification: validating the real-time correctness of computer-vision applications. The results that are produced will be made available to the world at large through open-source software. This software will include the new programming framework to be produced as well as tools for validating the real-time correctness of applications developed using this framework. In this project, a special emphasis will be placed on outreach to girls and women, as three female graduate students will be involved in the project. Such outreach will include: events involving the Graduate Women in Computer Science (GWiCS) group at the University of North Carolina (UNC), which hosts an annual research symposium targeted toward undergraduate women and other under-represented minorities; Tar Heel Hack, a hackathon for local middle and high school girls; the UNC Girls Who Code Club, which provides local girls in grades 6-12 with a community for learning about computer science; and the UNC Computer Science Department's annual Open House and Science Expo. These events will include hackathon projects as well as demos of a driving simulator and a sub-scale autonomous car.

The goal of this project is to explore vehicle platooning at scale in Smart and Connected Communities. The approach is the development of techniques and models that provide incentives for vehicles to join platoons and maintain their platoon memberships. Connected vehicle technology helps in forming vehicle platoons (virtual trains of vehicles traveling with small gaps between them) with benefits including improved energy efficiency, increased road capacity, and enhanced mobility. Vehicles in a platoon may benefit differently depending on where they are in the virtual train (for example, the lead saves less energy compared to a vehicle in the middle). As such, some vehicles may not be willing to join a platoon, or stay in one as better opportunities for platoon formation arise. This project explores how platoons with these competing goals will be formed and controlled, so as to understand how to motivate vehicles to participate in them. The Mcity testbed is part of the validation of the research work, and workshops involving local high-school and college students are planned. A stable platoon structure does not contain any coalition of vehicles who could increase their individual utilities by trading their platoon memberships. Given the dynamic nature of traffic streams, forming and maintaining stable platoon structures is a complex task, and requires accounting for both local and network-level conditions at the time of platoon formation. This proposal introduces a general framework that enhances optimal-control-based trajectory planning models by enabling them to also account for network level traffic conditions. This proposal further integrates stable platoon formation into the enhanced trajectory planning models, enabling them to incorporate both local and network-level information to form behaviorally compatible platoon structures that stay stable in a dynamic traffic stream.

The goal of this research is to enable a new era of low-energy mobile robotic Cyber-Physical Systems (CPS). The approach is the simultaneous design of the computing hardware with the computer algorithms, with input from the physics of the system. Applications include, but are not limited to, insect-size robotic bees for artificial pollination, robotic water striders for environmental monitoring, miniature underwater autonomous vehicles for inspection, orally-administered medical robotic vehicles that can intelligently navigate the digestive system, robotic gliders that can operate in the air or underwater for months at a time, and many more. The results will enable low-power computing for artificial intelligence and autonomy to complement the existing low-energy, miniature actuation and sensing systems that have already been developed. This will enable low-energy, miniature mobile robotic CPSs that can still provide provable guarantees on completeness, optimality, robustness and safety. This project will focus on the development of novel algorithms and novel computing hardware for miniature, energy-efficient mobile robotic CPS. The proposed research will enable low-energy computation for full autonomy by way of minimizing energy consumption during design time and run time, by simultaneously designing the algorithms and the computing hardware. Decision making algorithms will minimize computing energy during run time, for instance, by considering motions that may not require heavy computation for perception and planning. The project will demonstrate the new methods by constructing the smallest fully-autonomous aerial robotic vehicle ever built. We believe the proposed foundational research and the proposed demonstration will kickstart a new cyber-physical systems subfield at the intersection of the mobile robotics literature and the computing hardware (circuits) literature.

This project aims to develop non-intrusive and universal vibration sensing schemes that can detect the abnormal vibrations of a running machine. Towards this goal, the researchers propose a system that first uses the backscatter signals in commercial off the shelf RFID systems to accurately measure machine vibrations, and then uses machine learning and signal processing techniques to detect abnormal machine vibration patterns so that machine operators can be alerted to take actions before the machine fails. This project represents an emerging space driving new CPS and Internet of Things concepts for machinery safety. It can be used for the prognostic monitoring of not only indoor machines, but also outdoor appliances and civil infrastructures, such as drilling system monitoring, pumping system monitoring, pipeline system monitoring, and bridge monitoring. The proposed system is expected to impact manufacturing and economy. This project will bridge the communities between Computer Science and Mechanical Engineering; and foster interaction and communication among them. It will also facilitate the effort of the researchers on attracting and mentoring undergraduate students and underrepresented graduate students in research. Furthermore, the researchers will integrate the research results from this project into both undergraduate and graduate curricula. This project has two key technical objectives: to develop vibration measurement schemes using RFID systems and to develop abnormal vibration pattern recognition schemes based on the measured vibration signals. For vibration sensing, the basic idea is to measure the machine vibrations through random and low-frequency readings of the tag using the RFID reader, where each reading is viewed as one sampling of the vibration. For abnormal vibration pattern detection, the basic idea is to build base line models based on the measured vibration readings and then classify real time vibration readings of a running machine as being either normal or abnormal. The proposed system would have several advantages over prior art in machine health monitoring , e.g., nonintrusive, inexpensive, accurate, and easily deployable including in non-line-of-sight scenarios.

The goal of this project is to investigate how self-driving, electric vehicles transporting passengers on demand (a system referred to as autonomous mobility-on-demand, or AMoD) can enable optimized, coupled control of the power and transportation networks. The key observation is that the AMoD technology will give rise to complex couplings between the power and transportation networks, namely couplings between charging demand and electricity prices as people move around a city. The hypothesis is that by exploiting such couplings through control and optimization, AMoD systems will lead to lower electricity generation costs and higher integration levels of intermittent renewable energy resources such as wind and solar, while providing more convenient transportation. The results of this project will provide guidelines to transportation stakeholders and policy-makers regarding the deployment of autonomous vehicles on a societal scale, benefitting the U.S. economy by fostering clean and efficient future transportation systems. This project will devise theoretical models and optimization tools for the characterization of the aforementioned couplings and for the system-level control of AMoD with the power network in the loop. The key technical idea is to cast the coupled power and transportation networks in the formal framework of flow optimization, whereby city districts, charging stations, and roads are abstracted as nodes and edges of a graph, and the movements of customers, vehicles, and energy are abstracted as flows over such a graph. This project will then devise a control framework to optimize over the decision variables, e.g., vehicles' routes, charging decisions, and power generation schedules.

The goal of this project is to investigate how self-driving, electric vehicles transporting passengers on demand (a system referred to as autonomous mobility-on-demand, or AMoD) can enable optimized, coupled control of the power and transportation networks. The key observation is that the AMoD technology will give rise to complex couplings between the power and transportation networks, namely couplings between charging demand and electricity prices as people move around a city. The hypothesis is that by exploiting such couplings through control and optimization, AMoD systems will lead to lower electricity generation costs and higher integration levels of intermittent renewable energy resources such as wind and solar, while providing more convenient transportation. The results of this project will provide guidelines to transportation stakeholders and policy-makers regarding the deployment of autonomous vehicles on a societal scale, benefitting the U.S. economy by fostering clean and efficient future transportation systems. This project will devise theoretical models and optimization tools for the characterization of the aforementioned couplings and for the system-level control of AMoD with the power network in the loop. The key technical idea is to cast the coupled power and transportation networks in the formal framework of flow optimization, whereby city districts, charging stations, and roads are abstracted as nodes and edges of a graph, and the movements of customers, vehicles, and energy are abstracted as flows over such a graph. This project will then devise a control framework to optimize over the decision variables, e.g., vehicles' routes, charging decisions, and power generation schedules.

Each year the nation spends over $400 billion to power, heat and cool its buildings. Moreover, buildings are a major source of environmental emissions. As a result, even a modest improvement in energy efficiency of the nation's building stock would result in substantial economic and environmental benefits. In this project, the focus is on improving energy efficiency in commercial buildings because this sector represents a substantial portion of the energy usage and costs within the overall building sector. Enhancing the energy efficiency of commercial buildings is a challenging problem, due to the fact that centralized building systems -- such as heating, ventilation and air conditioning (HVAC), or lighting -- must be synthesized and integrated with individual inhabitant behavior and energy consumption patterns. This project aims to design, analyze, and test a cyber-physical and human-in-the-loop enabled control system that can drive sustained energy savings in commercial buildings. It brings together expertise in computational building science, eco-feedback, network theory, data science, and control systems to integrate physical building information and inhabitants with cyber (building-human) interaction models to enable intelligent control of commercial building systems. Specifically, this project will: 1) design an integrated cyber-physical system (CPS), called Building Information, Inhabitant, Interaction, Intelligent Integrated Modeling (BI5M), aimed at reducing energy usage in buildings; 2) assess the complex inter-relationships between and across physical building and inhabitant models, cyber building-human interaction and intelligent control models related to energy conservation behavior; and 3) empirically test and validate modules and the overall BI5M system at test-bed buildings on Stanford's campus and Google's office park. This research incorporates measurement (geospatial building data, energy use data), dynamics (inhabitant social networks), and control (enhanced user control of: plug-load devices, HVAC, lighting) into the BI5M system. The BI5M system is centered on a cyber Building Information Management (BIM) model of the building, and will encompass rigorous systems engineering that will explore relationships across the cyber-physical domains and develop new insights for how the scientific principles of cyber-physical systems can be used to influence the energy efficiency of commercial buildings through both occupant behavior and intelligent control. By integrating physical building information and inhabitants with cyber interaction modeling, the research aims to introduce an integrated human-in-the-loop control paradigm for commercial buildings. In addition to a testbed and validated CPS system for commercial buildings (BI5M), this project targets fundamental knowledge on: ontological components required to integrate dynamic data streams and control information into static building models; complex socio-spatial structures of inhabitants; insights into how building-human and human-human interactions impact inhabitant consumption behavior; and new control models that leverage input on the energy usage, spatial, social and behavior dynamics of inhabitants. The educational impacts of this project will extend to participants (students, faculty, Google employees in the test-bed buildings), as well as a broader student population through the integration of key insights from this work into courses/projects at all three collaborating universities (Stanford, Georgia Tech, and Columbia). The project team will also disseminate results to practitioners/policy-makers working in the building management space through an Outreach Workshop. Additionally, this project will broaden participation in computing fields through a diverse team and by partnering with the Girls Who Code nonprofit to integrate project data sets and tools into their activities.

Each year the nation spends over $400 billion to power, heat and cool its buildings. Moreover, buildings are a major source of environmental emissions. As a result, even a modest improvement in energy efficiency of the nation's building stock would result in substantial economic and environmental benefits. In this project, the focus is on improving energy efficiency in commercial buildings because this sector represents a substantial portion of the energy usage and costs within the overall building sector. Enhancing the energy efficiency of commercial buildings is a challenging problem, due to the fact that centralized building systems -- such as heating, ventilation and air conditioning (HVAC), or lighting -- must be synthesized and integrated with individual inhabitant behavior and energy consumption patterns. This project aims to design, analyze, and test a cyber-physical and human-in-the-loop enabled control system that can drive sustained energy savings in commercial buildings. It brings together expertise in computational building science, eco-feedback, network theory, data science, and control systems to integrate physical building information and inhabitants with cyber (building-human) interaction models to enable intelligent control of commercial building systems. Specifically, this project will: 1) design an integrated cyber-physical system (CPS), called Building Information, Inhabitant, Interaction, Intelligent Integrated Modeling (BI5M), aimed at reducing energy usage in buildings; 2) assess the complex inter-relationships between and across physical building and inhabitant models, cyber building-human interaction and intelligent control models related to energy conservation behavior; and 3) empirically test and validate modules and the overall BI5M system at test-bed buildings on Stanford's campus and Google's office park.

This research incorporates measurement (geospatial building data, energy use data), dynamics (inhabitant social networks), and control (enhanced user control of: plug-load devices, HVAC, lighting) into the BI5M system. The BI5M system is centered on a cyber Building Information Management (BIM) model of the building, and will encompass rigorous systems engineering that will explore relationships across the cyber-physical domains and develop new insights for how the scientific principles of cyber-physical systems can be used to influence the energy efficiency of commercial buildings through both occupant behavior and intelligent control. By integrating physical building information and inhabitants with cyber interaction modeling, the research aims to introduce an integrated human-in-the-loop control paradigm for commercial buildings. In addition to a testbed and validated CPS system for commercial buildings (BI5M), this project targets fundamental knowledge on: ontological components required to integrate dynamic data streams and control information into static building models; complex socio-spatial structures of inhabitants; insights into how building-human and human-human interactions impact inhabitant consumption behavior; and new control models that leverage input on the energy usage, spatial, social and behavior dynamics of inhabitants. The educational impacts of this project will extend to participants (students, faculty, Google employees in the test-bed buildings), as well as a broader student population through the integration of key insights from this work into courses/projects at all three collaborating universities (Stanford, Georgia Tech, and Columbia). The project team will also disseminate results to practitioners/policy-makers working in the building management space through an Outreach Workshop. Additionally, this project will broaden participation in computing fields through a diverse team and by partnering with the Girls Who Code nonprofit to integrate project data sets and tools into their activities.

Cyber-physical system (CPS) technologies, such as automated aircraft and cars, have become sufficiently complex that CPS software verification is now a major bottleneck in product development. This project examines new approaches for auto-generating reduced models of CPS software, in order to incorporate those models in analysis, for instance, in system-wide simulations or bug detection. This project will allow CPS software to be adapted and analyzed much more flexibility in comparison with state-of-the-art methods, which limit software developers by prohibiting use of many modern programming constructs and by penalizing iterative software improvements during the design process. The project's intellectual merit is the introduction of a theory of software-state observability, which will have wide utility for CPS analysis including in applications such as online bug detection. To this end the project concentrates on three specific aims: (i) the development of concepts for reduced-order software modeling based on static and dynamic analyses of CPS software programs, (ii) the formulation of a theory of software-state observability to enable state estimation across the boundaries of physical and software components, and (iii) the application of these theories to online bug monitoring for an open-source flight control system. The project represents a fundamental departure from the conventional treatment of software in a CPS, where software must be tightly specified in advance, where the program must be carefully verified to prove that it meets specifications, and where after final validation the software is assumed to be essentially free of bugs. Our approach permits developers much greater latitude in creating new CPS software by requiring reasonable but not excessive initial testing, as justified by better analysis tools and by online reliability monitoring. The result will enable more sophisticated and lower cost automated cars and unmanned aircraft. Results related to the project will be shared through archival publications and data will be made available online through Tufts University at https://tufts.box.com/s/aq305785bg2s3j29lls8u4wdpybzpcth. Software uploaded to this repository will be available under suitable licensing models (such as BSD or Apache). Data will be retained at least through the duration of this project.