Internet technology, originally developed to convey information, is increasingly being used to control and operate physical devices in homes, factories, medical facilities, and transportation systems, to name just a few application domains. In these more physically-grounded applications, the consequences of misbehavior of a system can be dire, involving not just loss or leakage of information, but loss of life. Historically, computers used in safety-critical systems have been completely isolated from the Internet to protect them from malicious hackers and unpredictable demands for their resources. But the benefits that Internet connectivity offers are irresistible, enabling far more sophisticated services. This project is developing a suite of mathematically-grounded design patterns and open-source software that leverages Internet technology while guaranteeing safety, reliability, and resilience to malicious attacks. One of these patterns endows a networked system with a stronger coordinated notion of time to ensure consistent behavior of the system even in face of unpredictable and uncontrollable delays in the network. Another of these patterns leverages edge computing, placement of computing services near the devices that use them, in hospitals, onboard in cars and trains, and in factories, for example, to mitigate the risks of relying on remote cloud-based services. Edge computers can ensure continuous safe operation even in face of Internet infrastructure collapse, as has occasionally happened under malicious attack. Technical Description: The Internet of Things (IoT) leverages Internet technology in cyber-physical systems, but the protocols and principles of the Internet were not designed for interacting with the physical world. For example, timeliness is not a factor in any widespread Internet technology, with Quality-of-Service (QoS) features having been routinely omitted for decades. Nevertheless, properties of the Internet could prove valuable in CPS, including a global namespace, reliable (eventual) delivery of messages, end-to-end security through asymmetric encryption, certificate-based authentication, and the ability to aggregate data from a multiplicity of sources in cloud-based warehouses. This proposal leverages recent developments that hold promise to bridge the gap, enabling the use of Internet technologies even in safety-critical, timing-sensitive applications such as factory automation and transportation. Specifically, we leverage time-sensitive network (TSN) technology; the use of smart gateways to isolate safety-critical services from best-effort services and to provide local proxies for cloud-based services; locally centralized, globally distributed authentication and authorization; and the development of coherent time-based semantics for distributed real-time services. The focus of this project will be on sound concurrent models of computation, on type-theoretic methods for ensuring correct composition, and on the realization of these formalisms in a software architecture that reconciles widely-used mechanisms in Internet services to hide uncontrollable latencies with the need for repeatable, testable, and robust real-time services in safety-critical systems. An open-source reference implementation will be delivered together with analytical papers on the formal properties of the models.

This project aims to develop innovative design automation methodologies and algorithms for software synthesis of cyber-physical systems (CPS), which have applications in key sectors such as automotive, aerospace, healthcare, and industrial automation. Software has become critical and drives future innovations for many such systems, but faces significant challenges in its development, in particular regarding the formulation, exploration and validation of timing constraints. The results from this project will address critical timing challenges in CPS software development, and lead to correct, predictable and efficient software implementations. In addition to disseminating the results through publications and workshops, the PI will collaborate with industry partners on transitioning the research findings into practice. Leveraging the research activities, the PI will develop an integrated education program that focuses on the interdisciplinary education of K-12, undergraduate and graduate students, through Lego Mindstorms labs development and contest organization, new CPS course development, and textbook writing. The project will develop, a software synthesis framework that addresses the timing challenges in CPS by quantitatively exploring timing constraints for multiple conflicting design metrics and across multiple abstraction layers, and using these timing constraints to drive the design space exploration. Developing the framework includes three closely-related research themes: (1) formulating and exploring timing contracts to co-design functionality and software architecture with respect to various design metrics (e.g., performance, security, schedulability) and to carry out hierarchical refinement across abstraction layers, (2) exploring the generation of software tasks from functional models and the mapping of those tasks onto hardware platforms with holistic timing consideration throughout the synthesis process, and (3) co-simulating functional and architectural models with explicit representation and evaluation of timing contracts to complement the proposed analytical synthesis algorithms.

U.S. economic growth, energy security, and environmental stewardship depend on a sustainable energy policy that promotes conservation,efficiency, and electrification across all major sectors. Buildings are the largest sector and therefore an attractive target of these efforts: current Federal sustainability goals mandate that 50% of U.S.commercial buildings become net-zero energy by 2050. A range of options exists to achieve this goal, but financial concerns require a data-driven, empirically-validated approach. However, critical gaps exist in the energy and water measurement technology, and indoorclimate control science, needed to benchmark competing options, prioritize efficiency investments, and ensure occupant comfort. To address these challenges, this project proposes a new kind of "peel-and-stick" sensor that can be affixed to everyday objects to infer their contributions to whole-building resource consumption. To use the sensors, occupants or building managers simply tag end loads like a ceiling light, shower head, or range top. The sensors monitor the ambient conditions around a load and, using statistical methods,correlate those conditions with readings from existing electricity, gas, or water meters, providing individual estimates without intrusive metering. The sensors are built from integrated circuit technology laminated into smart labels, so they are small, inexpensive, and easy-to-deploy. The sensors are powered by the same ambient signals they sense, eliminating the need for periodic battery replacement or wall power. Collectively, these properties address cost and coverage challenges, and enable scalable deployment and widespread adoption. The intellectual merit of this proposal stems from the insight that the transfer and use of energy (and other resources) usually emits energy, often in a different domain, and that this emitted energy is often enough to intermittently power simple, energy-harvesting sensors whose duty cycle is proportional to the energy being transferred or used. Hence, the mere activation rate of the sensors signalsthe underlying energy use. The power-proportional relationship between usage activity and side channel harvesting, when coupled with state-of-the art, millimeter-scale, nano-power chips and whole-house or panel-level meters, enables small and inexpensive sensor tags that are pervasively distributed with unbounded lifetimes. But, networking and tasking them, and making sense of their data, requires a fundamental rethinking of low-power communications, control, and data fusion to abstract the intermittent, unreliable, and noisy sensor infrastructure into actionable information. This project's broader impact stems from an integrated program of education, research, and outreach that (i) creates a smart objects focused curriculum whose classroom projects are motivated by research needs, (ii) provides research experiences for undergraduates and underrepresented minorities, (iii) mentors students on all aspects of successful research from articulating hypotheses to peer-reviewing papers,(iv) disseminates teaching materials on embedded systems and research pedagogy, (v) produces students who bridge disciplines,operating at the intersection of measurement science, information technology, and sustainability policy, and (vi) translates scientific discovery and technical knowledge into beneficial commercial products through industry outreach and internships, and (vii) engages with the National Labs to ensure that the research addresses pressing problems.

One of the challenges toward achieving the vision of smart cities is improving the state of the underground infrastructure. For example, large US cities have thousands of miles of aging water mains, resulting in hundreds of breaks every year, and a large percentage of water consumption that is unaccounted for. The goal of this project is to develop models and methods to generate, analyze, and share data on underground infrastructure systems, such as water, gas, electricity , and sewer networks. The interdisciplinary team of investigators from the University of Illinois at Chicago, Brown University, and Northwestern University will leverage partnerships with the cities of Chicago and Evanston, Illinois, to make the approach and findings relevant to their stakeholders. Research results will be incorporated in courses at the three institutions. Outreach efforts include events for K-12 students to develop awareness about underground infrastructure from a data and computational perspective. The results of the project will ultimately help municipalities maintain and renovate civil infrastructure in a more effective manner. Cities are cyber-physical systems on a grand scale, and developing a precise knowledge of their infrastructure is critical to building a foundation for the future smart city. This proposal takes an information centric approach based on the complex interaction among thematic data layers to developing, visualizing, querying, analyzing, and providing access to a comprehensive representation of the urban underground infrastructure starting from incomplete and imprecise data. Specifically, the project has the following main technical components: (1) Generation of accurate GIS-based representations of underground infrastructure systems from paper maps, CAD drawings, and other legacy data sources; (2) Visualization of multi-layer networks combining schematic overview diagrams with detailed geometric representations; (3) Query processing algorithms for integrating spatial, temporal, and network data about underground infrastructure systems; (4) Data analytics spanning heterogeneous geospatial data sources and incorporating uncertainty and constraints; (5) Selective access to stakeholders on a need-to-know basis and facilitating data sharing; and (6) Evaluation in collaboration with the cities of Chicago and Evanston.

The rapid evolution of data-driven analytics, Internet of things (IoT) and cyber-physical systems (CPS) are fueling a growing set of Smart and Connected Communities (SCC) applications, including for smart transportation and smart energy. However, the deployment of such technological solutions without proper security mechanisms makes them susceptible to data integrity and privacy attacks, as observed in a large number of recent incidents. If not addressed properly, such attacks will not only cripple SCC operations but also influence the extent to which customers are willing to share data. This in turn will make trustworthiness in SCC applications very challenging. To address this, a synergistic team of researchers from the US and Japan, under the JUNO2 program, will collaborate on this project, called STEAM (Secure and Trustworthy framework for integrated Energy and Mobility) to develop a framework to ensure data privacy, data integrity, and trustworthiness in smart and connected communities. The collaboration provides the project with a significant amount of automotive (transportation) data from Japan, and also access to a testbed in Japan. Although the target applications are smart mobility and smart energy (the choice is deliberate to exploit the complementary strengths of Japan and US in these two domains), the proposed techniques and solutions have wide applicability to other domains, such as smart healthcare. The novelty of the STEAM project lies in its integrated approach to handling security and trustworthiness in SCC applications. Specifically, the research team will develop innovative privacy-preserving algorithms and models for anomaly detection, trust and reputation scoring used by application providers for data integrity and information assurance. Towards that goal, they will study trade-offs between security, privacy, trust levels, resources, and performance using two exemplar applications in smart mobility and smart energy exchange in communities. Finally, they will design a modular, secure and trustworthy middleware architecture that implements privacy-preserving algorithms, resource constraints, and trustworthiness of data sources or content and decision-making schemes. The project has access to smart meter data from Texas, California, and Ireland and a large volume of automobile data from Japan. The evaluation plan includes integration of the project's anomaly detection and trustworthy decision-making algorithms into a smart vehicle route planning application and a transactive energy system in a plug-in electric vehicle testbed in Japan.

Automation is being increasingly introduced into every man-made system. The thrust to achieve trustworthy autonomous systems, which can attain goals independently in the presence of significant uncertainties and for long periods of time without any human intervention, has always been enticing. Significant progress has been made in the avenues of both software and hardware for meeting these objectives. However, technological challenges still exist and particularly in terms of decision making under uncertainty. In an autonomous system, uncertainties can arise from the operating environment, adversarial attacks, and from within the system. While a lot of work has been done on ensuring safety of systems under standard sensing errors, much less attention has been given on securing it and its sensors from attacks. As such, autonomous cyber-physical systems (CPS), which rely heavily on sensing units for decision making, remain vulnerable to such attacks. Given the fact that the age of autonomous CPS is upon us and their influence is gradually increasing, it becomes an urgent task to develop effective solutions to ensure the security and trustworthiness of autonomous CPS under adversarial attacks. The researchers of this project provide a comprehensive real-time, resource-aware solution for detection and recovery of autonomous CPS from physical and cyber-attacks. This project also includes effort to educate and prepare the community for the potential cyber and physical threats on autonomous CPS. With the observation that a thorough security certification of autonomous CPS will provide formal evaluation of autonomous CPS, the researchers in this project intend to develop methods to facilitate manufacturers for certifying security solutions. Toward this goal, the researchers will first develop new theories to understand the impact of physical and cyber-attack on system level properties such as controllability, stability, and safety. They will then develop algorithms for detection and recovery of CPS from physical attacks on active sensors. The proposed recovery method will ensure the integrity of sensor measurements when the system is under attack. Furthermore, a new analysis framework will be constructed that uses platform-based design methodology to represent the CPS and verifies it against design metric constraints such as security, timing, resource, and performance. The key contributions of this project towards autonomous CPS security certification include 1) a comprehensive study of relationship between attacks and system-level properties; 2) algorithms and their optimization for detection and automatic recovery of autonomous CPS from attacks; and 3) systematically quantifying impact of security on design metrics.

The objective of this research is to design a semi-automated, efficient, and secure emergency response system to reduce the time it takes emergency vehicles to reach their destinations, while increasing the safety of non-emergency vehicles and emergency vehicles alike. Providing route and maneuver guidance to emergency vehicles and non-emergency vehicles will make emergency travel safer and enable police and other first responders to reach and transport those in need, in less time. This should reduce the number of crashes involving emergency vehicles and associated litigation costs while improving medical outcomes, reducing property damage, and instilling greater public confidence in emergency services. At the same time, non-emergency vehicles will also be offered increased safety and, with the reduction of long delays attributed to emergency vehicles, experience reduced incident-related travel time, which will increase productivity and quality of life for drivers. Incorporating connected vehicles into the emergency response system will also provide synergistic opportunities for non-emergency vehicles, including live updates on accident sites, areas to avoid, and information on emergency routes that can be incorporated into navigation software so drivers can avoid potential delays. While the proposed system will naturally advance the quality of transportation in smart cities, it will also provide a platform for future techniques to build upon. For example, the proposed system could be connected with emergency care facilities to balance the load of emergency patients at hospitals, and act as a catalyst toward the realization of a fully-automated emergency response system. New courses and course modules will be developed to recruit and better prepare a future workforce that is well versed in multi-disciplinary collaborations. Video demos and a testbed will be used to showcase the research to the public. The key research component will be the design of an emergency response system that (1) dynamically determines EV routes, (2) coordinates actions by non-emergency vehicles using connected vehicle technology to efficiently and effectively clear paths for emergency vehicles, (3) is able to adapt to uncertain traffic and network conditions, and (4) is difficult to abuse or compromise. The project will result in (1) algorithms that dynamically select EV routes based on uncertain or limited traffic data, (2) emergency protocols that exploit connected vehicle technology to facilitate emergency vehicles maneuvers, (3) an automation module to assist with decision making and maneuvers, and (4) an infrastructure and vehicle hardening framework that prevents cyber abuse. Experiments will be performed on a testbed and a real test track to validate the proposed research.

The driving vision of this project is to detect Volatile Organic Compounds (VOCs) through ASTRO, a platform for autonomous 3-D data-driven mobile sensing via networked drones equipped with gas sensors. VOCs are hazardous to human health and the environment; they are released by explosions, gas leaks, and industrial accidents prevalent in low-income and under-resourced urban neighborhoods in close proximity to industrial processing plants, chemical refineries, and other sources of airborne pollutants. The project is located in an economically disadvantaged area of Houston, Texas. With Technology For All (TFA), the project team has a history of engaging the local community via broadband access, technology training, and connected health. The TFA wireless network already serves 1000's of community members in several square kilometers in Houston's East End via a mix of commercial Wi-Fi and software defined radios. The project targets realizing a high-resolution ground truth of environmental conditions in low-income urban areas which can impact emergency response procedures and environmental justice via policy and law. The project will develop a mobile app that alerts community residents of hazardous VOC concentrations near their current location. This project will impact urban areas with a demonstration of fusing next generation environmental sensing with next generation wireless access via networked drones. The project's objective is to realize an unprecedented resolution in VOC sensing by development and demonstration of ASTRO, a system for networked drone sensing missions without ground control. ASTRO will realize the unique capability to dynamically move sensors in 3-D according to real-time measurements. Consequently, networks of drones with on-board sensors can find and track VOC plumes, solely by coordinating among themselves, and without requiring a centralized ground controller. Two inter-related thrusts will realize this vision. The first is target detection, tracking, and modeling high VOC concentration clusters, targeting health and environmental safety. The second is development of the underlying principles and methodologies for data-driven mobile missions via drone networks. The project's outcomes will include lightweight machine learning methods that provide foundations for real-time distributed autonomous sensing with environmental and health objectives. These data sets will yield development of atmospheric models of VOCs at a finer resolution than is possible today. Moreover, the outcomes will also include methods for adaptive communication among the networked drones via software defined radios that can adapt their network topology and spectrum usage to realize mission objectives.

The potential economic and societal impacts of realizing fully autonomous cyber-physical systems (CPS) are astounding. If the Federal Aviation Administration (FAA) allows integration of unmanned aerial vehicles (UAVs) into the national civilian airspace, the private-sector drone industry is estimated to generate more than 100K high-paying technical jobs over a ten-year span and contribute $82B to the U.S. economy. Self-driving cars are predicted to annually prevent 5M accidents and 2M injuries, conserve 7B liters of fuel, and save 30K lives and $190B in healthcare costs associated with accidents in the U.S. Successful mission pursuit of such fully autonomous CPS hinges on possessing full situational awareness including precise knowledge of its own location. Current CPS are far from possessing this capability, particularly in dynamic, uncertain, poorly modeled environments where GPS coverage may be spotty, obscured, or otherwise impaired. This necessitates developing a coherent analytical foundation to deal with this emerging class of CPS, in which situational awareness and mission planning and execution are intertwined and must be considered simultaneously to address uncertainty, model mismatch, and compensate for potential GPS coverage gaps. This project is has four main objectives: (1) Analyze the observability of unknown dynamic, stochastic environments comprising multiple agents. This analysis will establish the minimum a priori knowledge needed about the environment and/or agents for stochastic observability. (2) Develop adaptation strategies to refine the agents models of the environment, on-the-fly, as the agents build spatiotemporal maps. Adaptation is crucial, since it is impractical to assume that agents have high-fidelity models describing the environment. (3) Design optimal, computationally efficient information fusion algorithms with performance guarantees. These algorithms will consider physically realistic nonlinear dynamics and observations with colored, non-Gaussian noise, commonly encountered in CPS. (4) Synthesize optimal, real-time decision making strategies to balance the potentially conflicting objectives of information gathering and mission fulfillment. This investigation will enable autonomous CPS to navigate complex tradeoffs, leading to autonomous identification and adoption of the optimal strategy. This research has far-reaching impact- it will evolve autonomous CPS from merely sensing the environment to making sense of the environment, bringing new capabilities in environments where direct human control is not physically or economically possible. The project has a vertically-integrated education plan spanning K-12, undergraduate, and graduate students. The project will engage economically disadvantaged middle and high school students in the same UAV testbed used for research verification. Also, research outcomes will be infused into new and existing undergraduate and graduate courses.

The number of systems developed for applications including package delivery via small unmanned aerial vehicles (UAVs) and self-driving cars, is growing. To ensure safe and reliable positioning, it is critical to address not only positioning accuracy, but also the confidence in accuracy, defined as integrity. Most of the positioning and navigation studies for autonomous vehicles have focused on only accuracy, but not integrity. However, navigating autonomous vehicles equipped with relatively low-cost sensors in complex and rapidly changing environments -- e.g., urban areas with Global Positioning System (GPS) signal blockage -- poses great challenges compared to flying aircraft in the open sky, where positioning integrity has been well addressed by the Federal Aviation Administration (FAA)-regulated aviation industry. This project aims to assess, monitor and improve positioning integrity for autonomous vehicles, such as UAVs and self-driving cars, and integrate the proposed research into education and outreach. The project involves a novel positioning integrity assessment and monitoring solution that is robust in GPS-challenged environments and is suitable for navigation sensor fusion. The investigator will (1) derive a new algorithm to directly assess and monitor GPS integrity in urban environments; (2) design an integrity monitoring framework for GPS sensor fusion using camera vision, LiDAR and inertial measurements; and (3) improve integrity by turning unwanted multi-path signals into a useful navigational source based on physical interaction with the environment. This CAREER development plan will also integrate an education plan with the research goals by broadening participation of under-represented groups, such as women, by fostering a female researcher community through organizing female social events at technical conferences; educating and informing the public about FAA rules and safety issues regarding flying UAVs; and outreach to K-12 students by demonstrating the results of the proposed research at the Illinois Engineering Open House and leading hands-on activities for various school girl camps.