Modern society relies heavily on systems that operate within strict timing requirements such as in engine control units in automobiles, aircraft avionics and navigation systems, programmable logic controllers in manufacturing plants, industrial control systems in the electricity sector, and many hundreds of others. The recent advent of autonomous cars, drones and internet-of-things (IoT) further expands the reach of these "real-time systems". The limitations of such devices viz., small computing power, less memory, limited battery power, has serious consequences for security, specifically, they become much harder to protect and defend. This research develops systematic security mechanisms for real-time embedded systems in critical applications to control what can be observed about them.
Computing systems that engage people physically with high degrees of autonomy need to provide rigorous guarantees of safety. Formal methods can been used on such systems to provide mathematical proofs to ensure correct behavior. However, machine learning and data-driven approaches are now an indispensable part of autonomous-systems design, and their reliance on highly nonlinear continuous functions and probabilistic reasoning has largely been at odds with the logical and symbolic-analysis frameworks in formal methods. As a result, the lack of formal assurance has become the key bottleneck that impedes the wider deployment and adoption of autonomous systems. This project targets this open challenge by developing formal synthesis and verification techniques for learning-based and data-driven control and planning methods for autonomous systems.
There is no question that indoor environments are often uncomfortable or unhealthy for occupants. This is an even more critical issue in healthcare facilities, where patients may experience the stressful effects of poor thermal, luminous, and acoustic environments more acutely. With complementary expertise from engineering and psychology, the proposed research is focused on creating a human-on-the-loop, responsive indoor environmental system with the potential to offer better quality of care in hospitals. The outputs of this project will have profound societal impacts on the wellbeing of both healthy individuals and on recovering sick individuals. Research outcomes will enable real time human-built environment interaction to minimize stress and optimize performance in any built environment, and ultimately lead towards economic benefits achieved through wellness and higher productivity. Improved indoor environmental quality in hospital settings will improve patient healing, which is an important societal benefit. Similar strategies can be used for educational facilities, and office buildings. This research encourages Broadening Participation through inclusion of individuals from underrepresented groups (female and Latinx Co-PIs), female and minority students, and a minority serving lead institution from an EPSCoR state. Results will be disseminated broadly through scientific publications and seminars, and K-12 outreach, including STEM competitions, and summer programs.
This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).
Today's increasingly sensorized agricultural farms are composed of sensors and drones generating copious volumes of data. Two trends in computation have catalyzed the "Internet-of-Small-Things", or IoST, in relation to digital and sustainable agriculture. First, the availability of inexpensive sensors that can withstand the rigors of agriculture. Second, the development of approximation algorithms for on-device computation of data analytics algorithms. In parallel, some demanding algorithms can be opportunistically offloaded to edge devices or to the cloud. There is an increasing trend to leverage the data from these "small" sensor nodes to actuate dependable, prompt, and resilient actions. Dependable means the algorithms need to deal with missing or corrupted data, network disruption, and node failures. Prompt refers to low-latency decisions, which are at par with the needs of the farmers or digital agriculture providers. The proposed project, Sirius, brings together IoST with machine learning (ML), and creates a compute fabric that is adaptive to the cyber and the physical conditions, and provides prompt actuation, resilient to noisy sensor nodes and communication channels.
One of the grand technical challenges of our generation is to get ready to feed 9 billion people by 2050 with sustainable use of water and chemicals. However, we are facing unprecedented challenges in adopting sustainable agricultural management practices, increasing production, keeping agriculture profitable and coping with deadly biotic and abiotic stresses and diseases as well as changing climate that threaten yield. This project aims to transform Cyber-Physical System (CPS) capabilities in agriculture to enable farmers to respond to crop stressors with lower cost, greater agility, and significantly lower environmental impact than current practices. The objective is to make foundational advances in AI, machine learning and robotics to individual plant-level sensing, modeling and reasoning. This enables small autonomous dexterous robots instead of the heavy farm equipment to monitor plants or small plots individually and treat them with minimum amount of chemicals. This also lowers the barrier to entry for small scale farmers, increases safety, minimizes runoff as well as soil compaction. This project includes a significant collaboration with the University of Illinois at Urbana-Champaign that is funded by the National Institute of Food and Agriculture (NIFA) within the U.S. Department of Agriculture.
The availability of vast amounts of operational and end-user data in cyber-physical systems implies that paradigm improvements in monitoring and control can be attained via learning by many artificial intelligence agents despite them possessing vastly different abilities. Engaging this heterogeneous agent base in the context of the smart grid requires the use of hierarchical markets, wherein end-users participate in downstream markets collectively through aggregators, who in turn are coordinated by an upstream market. The goal of this project is to conduct a systematic study of such market-mediated learning and control. This project aims at much deeper levels of participation from end-users contributing electricity generation such as rooftop solar, shedding load via demand response, and providing storage capabilities such as electric vehicle batteries, to transform into reliable distributed energy resources (DER) at the level of wholesale markets. A methodological theme is multi-agent reinforcement learning (MARL) by agents that control physical systems via actions at different levels of the hierarchy. Underlying the whole project are well-founded physical models of the transmission and distribution grids, which provide structure to the problem domain and concrete use cases. This project facilitates a deeper level of decarbonization in the electricity sector, and contributes to climate change solutions by engineering a flat, interactive grid architecture that allows significant DERs to provide electricity services to both local and regional grids. Engagement with a grid-level market operator enables the project to address a problem space of immediate relevance to the current electricity grid. The project also includes the development of educational materials on data-analytics and energy systems. Intrinsic to the program are efforts at outreach to involve high-school students via demonstrations and lectures based on the technology developed.
Understanding complex activity due to humans and vehicles in a large environment like a city neighborhood or even an entire city is one of the main goals of smart cities. The activities are heterogeneous, distributed, vary over time and mutually interact in many ways, making them hard to capture and understand and mitigate issues in a timely manner. While there has been tremendous progress in capturing aggregate statistics that helps in traffic and city management as well as personal planning and scheduling, much of this work ignores anomalous patterns. Examples include protests, erratic driving, near accidents, construction zone activity, and numerous others. Discovering and resolving anomalies is challenging for many reasons as they are complex and rare, depend on the context and depend on the spatial and temporal extent over which they are observed. There are potentially a large number of anomalies or anomalous patterns, so they are impossible to label and describe manually.
This project investigates fundamental techniques for building mathematical models that can be safely used to make trustworthy predictions and control decisions. Mathematical models form the foundation for modern Cyber-Physical Systems (CPS). Examples include vehicle models that predict how a car will move when brakes are applied, or physiological models that predict how the blood glucose levels change in a patient with type-1 diabetes when insulin is administered. The success of machine learning tools has yielded data-driven models such as neural networks. However, depending on how data is collected and the models are learned, it is possible to obtain models that violate fundamental physical, chemical, or physiological facts that can potentially threaten life and property. The approach of the project is to expose these model flaws through advanced analysis. The project seeks to broaden participation in computing through mentoring activities that will encourage undergraduate women and members of underrepresented minority groups to consider a career in research.
Advances in artificial intelligence (AI) implemented with neural networks and other machine learning techniques have transformed what computers can accomplish. Despite their potential, AI has had comparatively less impact on cyber-physical systems (CPS). Many CPS interact with the physical world where safety is important, so a solution with superior performance 99.9% of the time may still be unacceptable for a CPS. Unfortunately, AI systems are hard to prove correct ? it is difficult to trust the systems will always do what they are designed to do. The goal of this research is to advance the foundations of formal methods in order to make formal verification of AI-based CPS practical. If successful, the work will enable a justified trust in AI systems and allow AI to be applied within safety-critical processes that interact with the physical world. The project investigates approximation approaches where an AI component is replaced by an approximation with similar performance that is easier to verify.