Security and privacy concerns in the increasingly interconnected world are receiving much attention from the research community, policymakers, and general public. However, much of the recent and on-going efforts concentrate on security of general-purpose computation and on privacy in communication and social interactions. The advent of cyber-physical systems (e.g., safety-critical IoT), which aim at tight integration between distributed computational intelligence, communication networks, physical world, and human actors, opens new horizons for intelligent systems with advanced capabilities. These systems may reduce number of accidents and increase throughput of transportation networks, improve patient safety, mitigate caregiver errors, enable personalized treatments, and allow older adults to age in their places. At the same time, cyber-physical systems introduce new challenges and concerns about safety, security, and privacy. The proposed project will lead to safer, more secure and privacy preserving CPS. As our lives depend more and more on these systems, specifically in automotive, medical, and Internet-of-Things domains, results obtained in this project will have a direct impact on the society at large. The study of emerging legal and ethical aspects of large-scale CPS deployments will inform future policy decision-making. The educational and outreach aspects of this project will help us build a workforce that is better prepared to address the security and privacy needs of the ever-more connected and technologically oriented society. Cyber-physical systems (CPS) involve tight integration of computational nodes, connected by one or more communication networks, the physical environment of these nodes, and human users of the system, who interact with both the computational part of the system and the physical environment. Attacks on a CPS system may affect all of its components: computational nodes and communication networks are subject to malicious intrusions, and physical environment may be maliciously altered. CPS-specific security challenges arise from two perspectives. On the one hand, conventional information security approaches can be used to prevent intrusions, but attackers can still affect the system via the physical environment. Resource constraints, inherent in many CPS domains, may prevent heavy-duty security approaches from being deployed. This proposal will develop a framework in which the mix of prevention, detection and recovery, and robust techniques work together to improve the security and privacy of CPS. Specific research products will include techniques providing: 1) accountability-based detection and bounded-time recovery from malicious attacks to CPS, complemented by novel preventive techniques based on lightweight cryptography; 2) security-aware control design based on attack resilient state estimator and sensor fusions; 3) privacy of data collected and used by CPS based on differential privacy; and, 4) evidence-based framework for CPS security and privacy assurance, taking into account the operating context of the system and human factors. Case studies will be performed in applications with autonomous features of vehicles, internal and external vehicle networks, medical device interoperability, and smart connected medical home.

Cells, to carry out many important functions, employ an elaborate transport network with bio-molecular components forming roadways as well as vehicles. The transport is achieved with remarkable robustness under a very uncertain environment. The main goal of this proposal is to understand how biology achieves such functionality and leveraging the knowledge toward realizing effective engineered transport mechanisms for micron sized cargo. The realization of a robust infrastructure that enables simultaneous transport of many micron and smaller sized particles will have a transformative impact on a vast range of areas such as medicine, drug development, electronics, and bio-materials. A key challenge here is to probe the mechanisms often at the nanometer scale as the bio-molecular components are at tens of nanometer scale. The main tools for addressing these challenges come from an engineering perspective that is guided by existing insights from biology. The proposal will bring together researchers from engineering and biology and it provides an integrated environment for students. Moreover, it is known that an impaired transport mechanism can underlie many neurodegenerative maladies, and as the research here pertains to studying intracellular transport, discoveries hold the potential for shedding light on what causes the impaired transport. Robust infrastructure that enables simultaneous transport of many micron and smaller sized particles will have a transformative impact on a vast range of areas such as medicine, drug development, electronics, and bio-materials. Daunting challenges from the underlying highly uncertain and complex environments impede enabling robust and efficient transport systems at micro-scale. Motivated by transport in biological cells, this work proposes a robust and efficient engineered infrastructure for transporting micron/molecular scale cargo using biological constructs. For probing and manipulating the transport network, the proposal envisions strategies for coarse and fine resolution objectives at the global and local scales respectively. At the fine scale of monitoring and control, scarce and expensive physical resources such as high resolution sensors have to be shared for interrogation/control of multiple carriers. In this proposal, the principles for joint control, sensor allocation and scheduling of resources to achieve enhanced performance objectives of a high resolution probing tool, will be developed. A modern control perspective forms an essential strategy for managing multiple objectives. At the global scale, entire traffic will be monitored to arrive at real-time and off-line inferences on traffic modalities. Associated principles for dynamically identifying and tracking clusters of carriers and their importance will be built. This categorization of physical elements and their importance will determine the dynamic allocation of computational resources. Associated study of trade-offs will guide a combined strategy for allocation of computational resources and gathering of information on physical elements. Methods based on the reconstruction of graph topologies for reaching inferences that are suited to dynamically related time trajectories for the transportation infrastructure will be developed. The research proposed is transformative as it will enable a new transport paradigm at the cellular scale, which will also provide unique insights into intracellular transport where it will be possible to investigate multiple factors under the same experimental conditions.

The project will produce breakthroughs in the science of human-machine interaction and will produce lasting impacts on exercise machine technologies. The proposed Cyber-Enabled Exercise Machines (CEEMs) adapt to their users, seeking to maximize the effectiveness of exercise while guaranteeing safety. CEEMs measure and process biomechanical variables and generate adjustments to its own resistance, and generate cues to be followed by the exerciser. CEEMs are reconfigurable by software, which permits a wide range of exercises with the same hardware. Two prototype machines will be field-tested with the student-athlete population and used to validate project goals. The prototypes will be a valuable instrument for dissemination and outreach, as well as for student engagement. The outcomes of this research have repercussions beyond athletic conditioning: the same foundations and methodologies can be followed to design machines for rehabilitation, exercise countermeasure devices for astronauts, and custom exercise devices for the elderly and persons with disabilities. Thus, the project has the potential to improve health of society members at various levels. This research will contribute to the foundations of cyber-physical system science in the following aspects: biomechanical modeling and real-time musculoskeletal state estimation; estimation theory and unscented H-infinity estimation; control theory and human-machine interaction dynamics, and micro-evolutionary optimization for real-time systems. The proposed Cyber-Enabled Exercise Machines (CEEMs) are highly reconfigurable devices which adapt to the user in pursuit of an optimization objective, namely maximal activation of target muscle groups. Machine adaptation occurs through port impedance modulation, and optimal cues are generated for the exerciser to follow. The goals of the project are threefold: i) development of foundational cyber-physical science and technology in the field of human-machine systems; ii) development of new approaches to modeling, design, control and optimization of advanced exercise machines, and iii) application of the above results to develop two custom-built CEEMs: a rowing ergometer and a 2-degree-of-freedom resistance machine.

Parking can take up a significant amount of the trip costs (time and money) in urban travel. As such, it can considerably influence travelers' choices of modes, locations, and time of travel. The advent of smart sensors, wireless communications, social media and big data analytics offers a unique opportunity to tap parking's influence on travel to make the transportation system more efficient, cleaner, and more resilient. A cyber-physical social system for parking is proposed to realize parking's potential in achieving the above goals. This cyber-physical system consists of smart parking sensors, a parking and traffic data repository, parking management systems, and dynamic traffic flow control. If successful, the results of the investigation will create a new paradigm for managing parking to reduce traffic congestion, emissions and fuel consumption and to enhance system resilience. These results will be disseminated broadly through publications, workshops and seminars. The research will provide interdisciplinary training to both graduate and undergraduate students. The results of this research also fills a void in our graduate transportation curriculum in which parking management gets little coverage. The investigators will organize an online short training course in Coursera and National Highway Institute to bring results to a broader audience. The investigators will also collaborate with Carnegie Museum of Natural History to develop an online digital map and related educational programs, which will be presented in the museum galleries during public events. Technically, new theories, algorithms and systems for efficient management of transportation infrastructure through parking will be developed in this research, leveraging cutting-edge sensing technology, communication technology, big data analytics and feedback control. The research probes massive individualized and infrastructure based traffic and parking data to gain a deeper understanding of travel and parking behavior, and develops a novel reservoir-based network flow model that lays the foundation for modeling the complex interactions between parking and traffic flow in large-scale transportation networks. The theory will be investigated at different levels of granularity to reveal how parking information and pricing mechanisms affect network flow in a competitive market of private and public parking. In addition, this research proposes closed-loop control mechanisms to enhance mobility and sustainability of urban networks. Prices, access and information of publicly owned on-street and off-street parking are dynamically controlled to: a) change day-to-day behavior of all commuters through day-to-day travel experience and/or online information systems; b) change travel behavior of a fraction of adaptive travelers on the fly who are aware of time-of-day parking information and comply to the recommendations; and c) influence the market prices of privately owned parking areas through a competitive parking market.

This project aims to accelerate the deployment of security measures for cyber-physical systems (CPSs). A framework is proposed that combines anomaly identification approaches, which emphasizes on the development of decentralized cyber-attack monitoring and diagnostic-like components, with robust control countermeasure to improve reliability and maintain system functionality. Within this framework, the investigators will (1) implement hybrid observers and active attack detection methods exploiting system vulnerabilities; and (2) develop and integrate cyber-attack control countermeasure at the physical system level to guarantee functionality and resiliency in the presence of identified and unidentified threats. Specifically, this project focuses on applications to connected vehicle (CV) systems where vehicles are capable of sharing information via dedicated short range communication network, with the goal of improving fuel efficiency and avoiding collision. The project's final objective would be to create a cyber-secure vehicle connectivity paradigm that incorporates cyber-attack detection algorithms and executes integrated fault-tolerant countermeasures at the vehicle level to support vehicle system resiliency and accelerate the future commercialization of automated vehicles. The research solutions of this project will impact safety, security and reliability of networked CPSs by helping accelerate the adoption of threat identification and attack resilient control countermeasures at the system and network level. The specific application to connected and automated vehicles should lead to a future market acceptance of these vehicle technologies with a potential improvement in traffic conditions, vehicle and personal safety, and energy consumption. This project involves interdisciplinary research in cyber security for the development of more secure, scalable and reliable future networked CPSs. It proposes to conduct fundamental research on a model-based computational strategy that includes: 1) implement advanced threat models in a hybrid systems framework; 2) identify system and communication vulnerabilities especially in the dedicated short range communication network (DSRC) for CVs; 3) derive hybrid observer based cyber-attack detection algorithms based on stochastic quantized models and event triggered estimation; 4) establish active attack detection methods based on system vulnerabilities; 5) develop control counter measures for each CPS based on game theory and robust control methods; 6) derive control algorithms against malicious agents in the CV to avoid vehicle collisions; 7) develop computationally fast and distributed algorithms for the above six objectives; and 8) evaluate through simulation and experimental validation the capabilities and impact on the vehicle of the proposed strategies.

Security and privacy concerns in the increasingly interconnected world are receiving much attention from the research community, policymakers, and general public. However, much of the recent and on-going efforts concentrate on security of general-purpose computation and on privacy in communication and social interactions. The advent of cyber-physical systems (e.g., safety-critical IoT), which aim at tight integration between distributed computational intelligence, communication networks, physical world, and human actors, opens new horizons for intelligent systems with advanced capabilities. These systems may reduce number of accidents and increase throughput of transportation networks, improve patient safety, mitigate caregiver errors, enable personalized treatments, and allow older adults to age in their places. At the same time, cyber-physical systems introduce new challenges and concerns about safety, security, and privacy. The proposed project will lead to safer, more secure and privacy preserving CPS. As our lives depend more and more on these systems, specifically in automotive, medical, and Internet-of-Things domains, results obtained in this project will have a direct impact on the society at large. The study of emerging legal and ethical aspects of large-scale CPS deployments will inform future policy decision-making. The educational and outreach aspects of this project will help us build a workforce that is better prepared to address the security and privacy needs of the ever-more connected and technologically oriented society. Cyber-physical systems (CPS) involve tight integration of computational nodes, connected by one or more communication networks, the physical environment of these nodes, and human users of the system, who interact with both the computational part of the system and the physical environment. Attacks on a CPS system may affect all of its components: computational nodes and communication networks are subject to malicious intrusions, and physical environment may be maliciously altered. CPS-specific security challenges arise from two perspectives. On the one hand, conventional information security approaches can be used to prevent intrusions, but attackers can still affect the system via the physical environment. Resource constraints, inherent in many CPS domains, may prevent heavy-duty security approaches from being deployed. This proposal will develop a framework in which the mix of prevention, detection and recovery, and robust techniques work together to improve the security and privacy of CPS. Specific research products will include techniques providing: 1) accountability-based detection and bounded-time recovery from malicious attacks to CPS, complemented by novel preventive techniques based on lightweight cryptography; 2) security-aware control design based on attack resilient state estimator and sensor fusions; 3) privacy of data collected and used by CPS based on differential privacy; and, 4) evidence-based framework for CPS security and privacy assurance, taking into account the operating context of the system and human factors. Case studies will be performed in applications with autonomous features of vehicles, internal and external vehicle networks, medical device interoperability, and smart connected medical home.

Part 1: Upper-limb motor impairments arise from a wide range of clinical conditions including amputations, spinal cord injury, or stroke. Addressing lost hand function, therefore, is a major focus of rehabilitation interventions; and research in robotic hands and hand exoskeletons aimed at restoring fine motor control functions gained significant speed recently. Integration of these robots with neural control mechanisms is also an ongoing research direction. We will develop prosthetic and wearable hands controlled via nested control that seamlessly blends neural control based on human brain activity and dynamic control based on sensors on robots. These Hand Augmentation using Nested Decision (HAND) systems will also provide rudimentary tactile feedback to the user. The HAND design framework will contribute to the assistive and augmentative robotics field. The resulting technology will improve the quality of life for individuals with lost limb function. The project will help train engineers skilled in addressing multidisciplinary challenges. Through outreach activities, STEM careers will be promoted at the K-12 level, individuals from underrepresented groups in engineering will be recruited to engage in this research project, which will contribute to the diversity of the STEM workforce. Part 2: The team previously introduced the concept of human-in-the-loop cyber-physical systems (HILCPS). Using the HILCPS hardware-software co-design and automatic synthesis infrastructure, we will develop prosthetic and wearable HAND systems that are robust to uncertainty in human intent inference from physiological signals. One challenge arises from the fact that the human and the cyber system jointly operate on the same physical element. Synthesis of networked real-time applications from algorithm design environments poses a framework challenge. These will be addressed by a tightly coupled optimal nested control strategy that relies on EEG-EMG-context fusion for human intent inference. Custom distributed embedded computational and robotic platforms will be built and iteratively refined. This work will enhance the HILCPS design framework, while simultaneously making novel contributions to body/brain interface technology and assistive/augmentative robot technology. Specifically we will (1) develop a theoretical EEG-EMG-context fusion framework for agile HILCPS application domains; (2) develop theory for and design novel control theoretic solutions to handle uncertainty, blend motion/force planning with high-level human intent and ambient intelligence to robustly execute daily manipulation activities; (3) further develop and refine the HILCPS domain-specific design framework to enable rapid deployment of HILCPS algorithms onto distributed embedded systems, empowering a new class of real-time algorithms that achieve distributed embedded sensing, analysis, and decision making; (4) develop new paradigms to replace, retrain or augment hand function via the prosthetic/wearable HAND by optimizing performance on a subject-by-subject basis.

Many practical systems such as smart grid, unmanned aerial vehicles (UAVs) and robotic networks can be categorized as cyber physical systems (CPS). A typical CPS consists of physical dynamics, sensors, communication network and controllers. The communication network is of key importance in CPS, since it mimics the nerve system in the human body. Hence, it is critical to study how the communication network in CPS should be analyzed and designed. Essentially, communications stem from the uncertainty of system under consideration; random perturbations increase the system uncertainty, which is reduced by the control actions in CPS. It is well known that entropy is a measure of system uncertainty. A unified framework of entropy is used for CPS, in which random perturbations create entropy while communications and controls provide negative entropy to compensate the entropy generation. The intellectual merits are the novel framework of entropy for bridging the communications and control in CPS and the new design criterion based on the entropy of system state for CPS. The project's broader significance and importance are the education of various levels of students, the dissemination of results to public, and the impact on everyday life such as the improved agility and robustness of power grids. This project applies the framework of entropy to study the interdependencies of communications and control, thus facilitating the analysis and design of communications in CPS. The following tasks are tackled in the project: (a) Entropy Flow Based Communication Capacity Analysis in which communications in CPS is analyzed by studying the entropy fields in the physical dynamics, thus providing an estimation on the scale (bits/second) of communication capacity budget; (b) Communication Network Topology Design in which the design of the network topology (either physical or logical) is tackled through both optimization-based or heuristic approaches; (c) Online Network Resource Scheduling which refines the network resource scheduling during the operation using both optimization-based and heuristic approaches, within the framework of entropy fields; (d) Hardware Emulation Testbed which delivers a co-simulation testbed based on real time digital power simulator (RTDS) and a communication simulator, in the context of smart grids. Based on the research, new courses are developed. K-12 outreach and various levels of undergraduate/graduate educations are incorporated into the research.

Security and privacy concerns in the increasingly interconnected world are receiving much attention from the research community, policymakers, and general public. However, much of the recent and on-going efforts concentrate on security of general-purpose computation and on privacy in communication and social interactions. The advent of cyber-physical systems (e.g., safety-critical IoT), which aim at tight integration between distributed computational intelligence, communication networks, physical world, and human actors, opens new horizons for intelligent systems with advanced capabilities. These systems may reduce number of accidents and increase throughput of transportation networks, improve patient safety, mitigate caregiver errors, enable personalized treatments, and allow older adults to age in their places. At the same time, cyber-physical systems introduce new challenges and concerns about safety, security, and privacy. The proposed project will lead to safer, more secure and privacy preserving CPS. As our lives depend more and more on these systems, specifically in automotive, medical, and Internet-of-Things domains, results obtained in this project will have a direct impact on the society at large. The study of emerging legal and ethical aspects of large-scale CPS deployments will inform future policy decision-making. The educational and outreach aspects of this project will help us build a workforce that is better prepared to address the security and privacy needs of the ever-more connected and technologically oriented society. Cyber-physical systems (CPS) involve tight integration of computational nodes, connected by one or more communication networks, the physical environment of these nodes, and human users of the system, who interact with both the computational part of the system and the physical environment. Attacks on a CPS system may affect all of its components: computational nodes and communication networks are subject to malicious intrusions, and physical environment may be maliciously altered. CPS-specific security challenges arise from two perspectives. On the one hand, conventional information security approaches can be used to prevent intrusions, but attackers can still affect the system via the physical environment. Resource constraints, inherent in many CPS domains, may prevent heavy-duty security approaches from being deployed. This proposal will develop a framework in which the mix of prevention, detection and recovery, and robust techniques work together to improve the security and privacy of CPS. Specific research products will include techniques providing: 1) accountability-based detection and bounded-time recovery from malicious attacks to CPS, complemented by novel preventive techniques based on lightweight cryptography; 2) security-aware control design based on attack resilient state estimator and sensor fusions; 3) privacy of data collected and used by CPS based on differential privacy; and, 4) evidence-based framework for CPS security and privacy assurance, taking into account the operating context of the system and human factors. Case studies will be performed in applications with autonomous features of vehicles, internal and external vehicle networks, medical device interoperability, and smart connected medical home.