Safety critical medical systems increasingly aim to incorporate learning-enabled components that are developed using machine learning and AI. While the impact of these learning-enabled medical cyber-physical systems (LE-MCPS) are revolutionizing personalized patient care and health outcomes, assuring their safety and efficacy remains a formidable challenge. Existing model-based design paradigms for learning-enabled cyber-physical systems require an abundance of ?clean? data or high-fidelity simulators ? unfortunately, LE-MCPS do not have that luxury. Consequently, LE-MCPS development strongly depends on experimentation to generate data for design and assurance. The ethical and economic constraints of working in safety-critical medical applications necessitate experimentation efficiency. Yet, experimental design and learning-enabled component design are often weakly coupled -- which contributes to inefficiencies, increased development costs, and increased patient risk. This CAREER proposal aims to develop foundations and tools for assuring learning-enabled medical cyber physical systems (MCPS) by bridging-the-gap between experimentation and model-based design. Specifically, the research focuses on leveraging model-based design techniques to address foundational challenges associated with experimental design (ante-experimentation), protocol execution (during experimentation), and system assurance (post-experimentation). The project?s broader significance will advance the state-of-the-art in medical system design, accelerate learning-enabled CPS (LE-CPS) innovation, and provide abundant interdisciplinary and use-inspired education opportunities and outreach activities.

The goal of this project is to develop foundations and tools for assuring LE-MCPS by bridging-the-gap between experimentation and model-based design. The proposed research will result in a high-assurance LE-CPS design framework spanning ante-, intra-, and post-experimentation. Prior to experimentation, this work will develop foundational techniques to address gaps in traditional experimental designed exposed by high-assurance LE-CPS design. During experimentation, new platforms and capabilities will be realized that can support tamper-evident run-time experimental data curation for assuring LE-CPS. After experimentation, techniques that leverage historical evidence and experimental data will maximally assure LE-CPS designs. Foundations developed in the project are prospectively evaluated in industrial LE-MCPS applications. While the research is motivated by medical scenarios, the developed technologies are immediately applicable to a wide range of LE-CPS applications.

The long-term goal of this project is to build flexible models and efficient algorithms for large-scale, multi-agent, and uncertain cyber-physical systems. In settings such as traffic management, for example, practitioners face fundamental challenges due to complex dynamics, hierarchical influence, noncooperative actors, and hard-to-model uncertainty. Strong simplifying assumptions have become essential: for instance, many theoretical models of road networks take the form of static, deterministic, and/or aggregative games. In these instances, static assumptions make it possible to predict the aggregate impact of decisions such as tolling on traffic patterns. However, neglecting temporal dynamics and feedback effects can lead city planners to make myopic decisions, which may have unintended consequences as drivers adapt to one another's behavior over time. This project develops theoretical and algorithmic techniques to address some of the underlying challenges and will also support mentoring of graduate and undergraduate researchers, development of undergraduate course material, and outreach to local underrepresented communities. This NSF CAREER project aims to develop a sound algorithmic basis for game-theoretic inference and design in dynamic and multi-agent CPS. The specific goals of this project are threefold. The first goal is to formalize and solve a set of structural inference problems in noncooperative games that arise in transportation. For example, one such problem is to discover hierarchies of influence among decision-makers from observations of their actions. The second goal of this project is to design dynamic, time-varying mechanisms which influence agents? decisions and induce desired outcomes. In transportation systems, these mechanisms correspond to tolls, bus routes, timetables, etc. The third and final goal considers stochastic variants of the aforementioned games and aims to develop a computationally-tractable theory of time-varying, feedback decision-making in these settings. This project will enable the analysis and design of cyber-physical systems which interact with one another in complex hierarchies and enable planners and regulators to guide these systems toward desired outcomes. Theory and algorithms will be validated in a physical laboratory testbed which emulates urban driving, via large-scale simulation of traffic in the city of Austin and using French air traffic management data

This NSF project aims to solve the problem of oscillations faced by power grids with high penetration of renewable energy that are disrupting system operations. The project will bring transformative change in fundamental understanding of such phenomena and propose new control strategies to damp the oscillations by leveraging the cyber layer of the power grid including sensors and communication network. This will be achieved by novel approaches of modeling the cyber physical power grid that can capture such phenomena, and centralized and decentralized controls that can damp such oscillations even in presence of anomalies in sensor measurements including cyber-attacks. The intellectual merits of the project include development of computationally manageable cyber physical models, novel sensor grouping and algorithms for data recovery from corruption, and control methods that do not rely on detailed renewable plant models. The broader impacts of the project include solving a major impediment of renewable energy integration that can help tackle climate change, integrating the proposed research in summer camps with high school students, offering summer internships for underrepresented minorities, informing curricula, and student engagement through Penn State?s Center for Engineering Outreach and Inclusion. The proposed project has three key thrusts addressing the sub- and super-synchronous oscillations (SSOs) in presence of inverter-based resources (IBRs). First, a scalable, computationally manageable, and linearizable dynamic phasor-based modeling framework with unbalance simulation capability for grids with high penetration of IBRs is proposed, which is coupled to a realistic cyber layer model with data packet drops and delays. Second, a centralized remedial action scheme based damping control is proposed that relies on three steps ? (a) offline phasor measurement unit (PMU) placement, online dynamic signal grouping, and signal recovery from sparse and non-sparse corruption, (b) detection and source localization of SSOs using dissipating energy flow (DEF) approach, and (c) determination of generation re-dispatch through a novel DEF sensitivity calculation. Third, decentralized modulation-based corruption-resilient SSO damping is proposed based on model predictive control (MPC) framework that does not require knowledge of IBR models, wherein the computational burden will be reduced by estimating a map by offline training of neural networks.

Understanding the root cause of behavior is imperative for informed decision-making and preventing ineffective or biased policies. Currently, most AI-based learning and control modules embedded in cyber-physical systems (CPS) rely on statistical correlation rather than causality for decision-making. This not only results in incorrect decisions but also hinders the interpretability of learning, limiting transferability and scalability. This CAREER proposal aims to bridge the gap between causal inference and the growing capabilities of reinforcement learning (RL) in CPS. The proposed methods are transformative to a wide range of CPS applications, enabling more efficient and effective decision-making processes in autonomous and swarm CPS such as self-driving cars, drones, industrial robots, and swarm robots. This NSF CAREER proposal proposes a set of temporal causal RL and control approaches for CPS by leveraging the reasoning capabilities of temporal logics and causal diagrams in single-agent, multi-agent, and swarm system settings. The tools we develop will be implemented on multiple CPS testbeds and integrated with the proposed education plan. The proposed algorithms have the following unique and innovative features. Firstly, we will develop computationally efficient tools that can discover temporal causal knowledge from both observational and interventional data of a CPS in performing RL to improve the sampling efficiency and transferability. Secondly, we will develop multi-agent RL approaches for CPS in cooperative, non-cooperative, and incomplete information stochastic game environments where temporal causal knowledge is discovered in a distributed way for expediting RL. Lastly, we will develop scalable RL-based control methods for swarm systems utilizing temporal causal reasoning over agent-level features and swarm-level features such as densities and generalized moments. The education plan will impact the next generation of CPS and AI engineers and researchers through AI-assisted adaptive and interactive teaching, temporal-logic-based educational games, online interactive educational website design for temporal causal RL, and workshops and webinars with industrial partners.

The Congestion Impacts Reduction via CAV-in-the-loop Lagrangian Energy Smoothing (CIRCLES) project aims to reduce instabilities in traffic flow, called "phantom jams," that cause congestion and wasted energy. If you have ever encountered a temporary traffic jam for no apparent reason, this might have been a phantom jam that occurred naturally because of human driving behavior.

Prior work on closed-course testing demonstrated that phantom jams can be reduced using autonomous vehicle technologies and specially-designed algorithms. The CIRCLES project seeks to extend this technology to real-world traffic, where reducing these negative traffic effects could provide ≥10% energy savings.

In 2022, the CIRCLES team conducted the largest open-road traffic experiment of CAVs designed for wave smoothing, in Nashville, TN. The resulting experiment produced news articles with an audience reach of over 1 Billion.

Please visit the CIRCLES website for a comprehensive description of the project, its scope and scale, and resulting data, videos and other products.

Industrial Internet of Things (IIoT) systems are used in a wide range of mission- and safety-critical applications, thus imposing stringent requirements on the security of the underlying communication infrastructure. An IIoT network consists of multiple communication parties and follows a two-way communication model, including delivering sensing data on the uplink and transmitting control messages on the downlink. Tampered sensing data or control messages by outside attackers will result in wrong decisions, potentially causing significant harm. The recent trend in industrial automation to connect interdependent industrial plants together to provide decentralized, verifiable and immutable services further exacerbates the problem. This project aims to design 1) efficient signature schemes to support verifiable authenticity, integrity, and uniformity for intra-plant two-way communications, and 2) hierarchical and scalable blockchain protocols to support inter-plant immutable services. The close collaboration of the research teams will lead to a publicly available IIoT-enabled advanced manufacturing testbed, effective dissemination of research results among practitioners, and initiation of technology transfer.

To address existing limitations, the proposed secure communication framework aims to (i) ensure authenticity, integrity, and uniformity of sensing data in IIoT networks by designing novel signature schemes that are fast and efficient for both the signer and the verifier; (ii) enable public-key cryptography (PKC)-based fast control message authentication by extending the control border of IIoT networks to the cloud/Internet and solving the new security challenges; and (iii) provide inter-plant immutable services by developing a hierarchical blockchain structure and scalable lightweight consensus protocols. The proposed solutions will be implemented and deployed on a unique IIoT-enabled advanced manufacturing system testbed for thorough design validation and performance evaluation. Successful design, implementation and demonstration of the proposed security solutions should advance the adoption of IIoT network infrastructure, accelerate the transformation of legacy security architectures to PKC-based security architectures and lift the security protection of the industrial communication infrastructure to the next level.

This project proposes a novel safety system that enables real-time prediction for safety risks near highway work zones, through recent advances in Artificial Intelligence (AI). The proposed safety system provides real-time notification to highway workers through smart glasses when a work zone intrusion is about to happen. In particular, this project focuses on short-duration highway work zones which cause higher safety risks due to lack of proper safety mechanisms. This project enhances the health and prosperity of the nation by making highways safer places for workers and preventing potential fatalities or injuries caused by highway work zones. This project departs from existing reactive safety systems to a true proactive safety system. It makes fundamental contributions in real-time deep learning algorithm design and processing, edge computing, and assisted reality systems to enable real-time prediction of work zone intrusions and notification of highway workers. The proposed worker-in-the-loop safety system will be co-designed and co-created with the direct help of highway work zone workers, leading industries, and human factors experts to identify the best feedback mechanisms for alarming workers regarding upcoming safety risks. This project will play a key role in the development of the next generation cyber-physical systems with powerful edge computing for many emerging safety and security-related applications.

In the automotive industry today, companies are fiercely competing to field ever more sophisticated autonomous features in their product lines. The hoped-for culmination of this competition is full autonomy at mass-market scales. The stakes here are high: the companies (and countries) that get there first will be in a commanding position to influence how autonomy-related capabilities evolve for decades to come. This high-stakes competition has resulted in significant pressure to innovate quickly with respect to key technologies for autonomous driving, such as perception and decision-making capabilities. This pressure has led to a ?black-box? approach to system design, with off-the-shelf software and hardware components, originally intended for other contexts, repurposed to implement autonomous-driving functions. One of the most widely used repurposed black-box components is ROS (the Robot Operating System). ROS enables separately developed software programs that implement different functions (e.g., camera-based perception, correct lane following, etc.) to be combined to form a system that provides broader capabilities (e.g., a car that drives itself). Unfortunately, as its name suggests, ROS was originally designed and implemented to support the development of robotics applications, which have very different requirements from autonomous vehicles. As a result, ROS lacks features needed to ensure safe automotive system designs. A key issue here is a lack of support for ensuring real-time safety, i.e., that certain functions (e.g., braking) are performed ?on time? (e.g., before an obstacle is hit). This project is directed at producing an alternative to ROS that takes real-time safety as a first-class concern. Despite its name, ROS is really not an operating system (OS) but rather a set of user-level middleware libraries that facilitate constructing processing graphs typical of robotics applications. These libraries support modular system development via a publish/subscribe (pub/sub) notion of message communication between graph nodes that allows different software packages to be loosely coupled. This loose coupling enables software reuse, which has been a key to ROS?s success in enabling rapid innovation. ROS?s success convincingly demonstrates the importance of pub/sub in fueling innovation in autonomy. However, pub/sub must be safe to apply. This project is directed at this very issue, specifically in the context of multicore+acclerator platforms as used in autonomous vehicles. In such a platform, a CPU-only multicore computer is augmented with co-processors like graphics processing units (GPUs) that can speed up certain mathematical computations that commonly occur in AI-based software for autonomy. The specific aim of this project is to produce a pub/sub alternative to ROS that facilities real-time safety certification. Key research tasks include resolving fundamental resource-allocation concerns at the OS and middleware levels, producing analysis for validating response-time bounds in real-time pub/sub graphs, producing a reference pub/sub middleware implementation, and experimentally comparing this implementation to ROS. While evolving ROS itself is beyond the scope of this project, this project will expose fundamental tradeoffs of relevance to such an evolution.

The emergence of intelligent technologies is enabling a new era of connection between community residents and the surrounding environments, both in the United States and around the world. With the new wave of growth in urban areas, ensuring public safety is an essential precursor toward "smart" cities and communities. This project proposes a novel "intelligent" policing technology as a transformative solution to efficiently enhance law enforcement, while minimizing unnecessary interactions and maintaining resident privacy. The proposed technology offers a network of smart cameras that do not require continuous monitoring, but instead are trained to generate alerts on the spot in real-time. Since the cameras identify behaviors and not identities, they can reduce biases, minimize false alarms, and protect personal privacy. The intelligent policing technology will be co-designed and co-created with the direct help of community residents, neighborhood leaders, and local business owners, as well as agencies including the City of Charlotte, and local law enforcement agencies in Charlotte-Mecklenburg and Gaston counties. The proposed research makes fundamental advances in multiple areas from computer vision, computer architecture, and real-time edge computing, as well as criminology and community-technology interaction. It paves the path for bringing the recent advances in deep learning and data analytics to enhance the safety and security of communities without jeopardizing the privacy of residents. To this end, this project formulates social-technical advances to efficiently analyze and assist communities and governing agencies in making real-time, smart reactions. The project enables real-time vision processing near the cameras (edge nodes) and cooperative processing over the edge network. At the same time, the proposed research interprets, formalizes, and models public safety and security events to be machine detectable, reducing biases, and enabling broad-based community support and trust. By demonstrating the use of powerful emerging edge computing technologies, the project will highlight the applicability and adaptability of such technologies to tackle many community challenges and broader smart cities and cyber-physical systems (CPS) applications, including smart transportation and pedestrian safety. Additionally, the proposed community-based pilots will serve as exemplars to other communities across the nation.

The importance of water to civilization is unquestionable; over centuries, this critical community lifeline has become complex with multiple subsystems (drinking water(DW), wastewater(WW), and stormwater(SW)) to import, deliver and haul away water. Today, these infrastructures are designed and operated separately by an array of local governments, water districts, and regulatory agencies - all subjected to stress caused by aging, urbanization, failures, extreme events, and demand/supply variabilities. This proposal brings together an interdisciplinary team of researchers and practitioners in computer science, civil engineering, public policy, and social ecology to create a Smart Water data-exchange framework, SWADE, that will serve as a repository and sandbox for collecting, sharing, exploring, analyzing, and curating information about diverse community water systems. SWADE will utilize recent advances in IoT and big data systems to create a holistic understanding of these interacting platforms - the framework integrates static and dynamic data from infrastructures and communities with domain-specific models/simulators and analytics services to create new levels of efficiency and resilience in co-executing systems. Innovative research will address tradeoffs (e.g. cost, accuracy) in data collection, develop semantic approaches for joint data representation and storage, explore data cleaning and refinement mechanisms, promote community engagement to drive policy-based exchange to address data-sharing barriers and design novel analytics to understand resilience and societal impact of water policies. Innovations to existing infrastructures require public acceptance; to achieve this, the team includes practitioners at water agencies in Southern California (e.g. Orange County, Irvine, Los Angeles) and Illinois who will help create and instantiate the SWADE framework; interactions with agencies in Florida and Maryland will help ensure transferability of SWADE. Through SWADE, communities around the nation can learn and share lessons with each other, experiment with sample data/networks to understand design choices as they plan future investment in water systems. This project can help guide policy research on information interchange in other complex community infrastructures (e.g. water-energy-food nexus, transportation networks) where socioeconomic and geopolitical constraints play a role in determining what can be shared and exchanged. Educational outreach will leverage efforts of the Water UCI Center, and campus programs including RET, REU, K-12, and women in STEM programs at UCI and SDSU. Our programs will focus on promoting broader participation by allowing citizens from diverse backgrounds and perspectives to contribute to the essential research mission of ensuring safe and reliable water services for the future.