The overarching goal of the proposed research is to derive critical information and characterization of large scale generic nonlinear dynamical systems using limited observables. In the present state-of-the-art in data-driven dynamical system analysis, all the underlying state measurements and the time evolution of these states are required. Access to all of the dynamical states measurements in real-world is impossible or expensive. The objective of the proposal is to develop data-driven tools for dynamic system identification, classification and root-cause analysis of dynamic events, and prediction of system evolution. The research team will specifically conduct research on using available measurements to perform near real-time applications for various dynamic events that occur in electric power systems. The data analytics proposed are applicable to general non-linear dynamic systems and can be easily applied to other cyberphysical systems (CPS). More broadly, there is a large effort in the CPS and control community to model real world systems that we all interact with on a daily basis (such as transportation systems, communication networks, world wide web, etc.) as dynamical systems and thus, the theory and techniques developed through this project will enable online monitoring of these critical systems, allowing operators to quickly analyze these systems for any unstable/anomalous behavior from minimal data streams. The project will promote various educational and outreach activities including developing new courses, short courses, activities in schools, and scholarships for women and underrepresented minority students. 

Overview: The goal of this proposal is to develop operator theoretic data analytics techniques for dynamic systems with limited measurements to identify the underlying non-linear dynamical system and characterize their behavior such as causal interactions between constituent components, stability monitoring, identifying targets for control. The proposed research is in the domain of "Technology for cyber-physical systems". The novelty of the proposed methods is that they do not require the dynamic states but can utilize system outputs, making it applicable to real-world dynamical systems. Power systems are rapidly evolving with increased deployment of sensors like the phasor measurement units (PMUs) that have high accuracy and high sampling frequencies (up to 120 Hz). These measurements will be used to develop an equivalent linear representation in a higher dimensional function space that can be used for online identification and characterization of nonlinear dynamics of the power grid. Further, machine learning techniques will be formulated to learn effective dictionary functions for the scalable deployment of proposed method. Using the proposed system identification method, the project will develop the theory and methodology for data-driven Information Transfer based causality mapping for detection and localization of system stress and dynamic coupling between the systems components. Specific applications for power grids will include stability monitoring, trajectory prediction and identification of targets for controlling adverse dynamic behavior. The methods are evaluated by an integrated power-cyber co-simulator (IPCC) that integrates power transmission, distribution and communication systems to generate synthetic sensor data for large systems under various dynamic scenarios. The IPCC will be able to model intermediate communication networks that cause measurement inconsistencies like delays, packet drop, etc. The Iowa State University's hardware in the loop cyber-physical testbed will be used to validate and evaluate some of the online applications like stability monitoring and trajectory prediction for large power grid topologies.

Smart home products have become extremely popular with consumers due to the convenience offered through home automation. In bridging the cyber-physical gap, however, home automation brings a widening of the cyber attack surface of the home. Research towards analyzing and preventing security and safety failures in a smart home faces a fundamental obstacle in practice: the poor characterization of home automation usage. That is, without the knowledge of how users automate their homes, it is difficult to address several critical challenges in designing and analyzing security systems, potentially rendering solutions ineffective in actual deployments. This project aims to bridge this gap, and provide researchers, end-users, and system designers with the means to collect, generate, and analyze realistic examples of home automation usage. This approach builds upon a unique characteristic of emerging smart home platforms: the presence of "user-driven" automation in the form of trigger-action programs that users configure via platform-provided user interfaces. In particular, this project devises methods to capture and model such user-driven home automation to generate statistically significant and useful usage scenarios. The techniques that will be developed during the course of this project will allow researchers and practitioners to analyze various security, safety and privacy properties of the cyber-physical systems that comprise modern smart homes, ultimately leading to deployments of smart home Internet of Things (IoT) devices that are more secure. The project will also produce and disseminate educational materials on best practices for developing secure software with an emphasis on IoT devices, suitable for integration into existing computer literacy courses at all levels of education. In addition, the project will focus on recruiting and retaining computer science students from traditionally underrepresented categories.

This project is centered on three specific goals. First, it will develop novel data collection strategies that allow end-users to easily specify routines in a flexible manner, as well as techniques based on Natural language Processing (NLP) for automatically processing and transforming the data into a format suitable for modeling. Second, it will introduce approaches for transforming routines into realistic home automation event sequences, understanding their latent properties and modeling them using well-understood language modeling techniques. Third, it will contextualize the smart home usage models to make predictions that cater to security analyses specifically and develop tools that allow for the inspection of a smart home?s state alongside the execution of predicted event sequences on real products. The techniques and models developed during the course of this project will be validated with industry partners and are expected to become instrumental for developers and researchers to understand security and privacy properties of smart homes.

Propelled by the growth in demand for artificial intelligence-enabled applications, the past decade has witnessed the emergence of Collaborative Cyber-Physical Learning Systems (CCPLS). CCPLS carry out distributed, learning-based processing tasks through coordination among Cyber-Physical System (CPS) devices, and are envisioned to provide critical functionality across the commercial and defense sectors in the next several years. However, the data generated by CCPLS is often large-scale, high-dimensional, heterogeneous, and time-varying, which poses critical challenges for intelligence modeling. Concurrently, unmanned vehicles, in particular Unmanned Aerial Vehicles (UAVs), have shown promise of scaling up information-sharing in CCPLS, especially in under-served regions such as rural areas. The project's novelties are in establishing a concrete foundation for UAV-CCPLS integration that unifies the associated learning, networking, and communication design aspects around appropriate intelligence metrics. The project's impacts are the development of UAV-assisted CCPLS for smart agriculture tasks, as well as advancing the manufacturing of UAVs and other unmanned vehicles tailored for CCPLS. Project outcomes will be disseminated by releasing open-source software and research videos and tutorials. The investigators will further engage in Curriculum development, diversity, and outreach activities including mentoring undergraduate researchers. Research investigations center around three interconnected thrusts. Thrust 1 develops a novel UAV-assisted intelligence framework for CCPLS and introduces a precise, task-oriented notion of data dynamics and heterogeneity. Additionally, this thrust develops a `learning for learning? framework that aims to predictively estimate the rate of data dynamics. Thrust 2 investigates methodologies for jointly optimizing resource utilization and intelligence quality through co-design of UAV trajectories and UAV-to-CPS network establishment. The data dynamics framework from Thrust 1 is integrated into this design through an online, network-aware sequential decision-making framework. Finally, Thrust 3 develops CCPLS communication protocols based on learning-aware uplink and downlink wireless beamformers and over-the-air aggregation methods. These protocols are tailored to the specific needs of the UAV-assisted learning systems, e.g., the transmission of noisy information over UAV-to-UAV and UAV-to-access point communication links. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The Ninth IEEE International Conference on Smart Computing (SMARTCOMP 2023) will be held in Nashville, TN, USA on June 26-30, 2023. IEEE SMARTCOMP 2023 conference offers a valuable opportunity to present cutting-edge computing and engineering research results, to learn about the state-of-the-art in smart and connected computing and communities, and to train US-based undergraduate, master?s, doctoral, and post-doctoral students (from computer science and engineering disciplines) through the main conference and other associated events (e.g., student forum, workshops, demo, posters, and industry sessions). SMARTCOMP 23 will be the first in-person meeting since the pandemic and as such will facilitate research collaborations in this important research space.This grant will support the travel of 20 US-based students to attend the IEEE SMARTCOMP 2023 conference in-person in Nashville, TN, USA. SMARTCOMP is the premier conference on smart computing that is emerging as an important multidisciplinary area. Smart Computing can be broadly classified into two major topics: (1) how to design and build smart computing and service systems, and (2) how to use computing and engineering technology for resource sustainability to improve the human experience. Applications of smart computing span trans-disciplinary boundaries such as transportation, energy, sustainability, structural health, environmental protection, healthcare, security, and so on. Advancements in wireless mobile communications, nanotechnology, cyber-physical systems (CPS), internet of things (IoT), sensor networking, cognitive/intelligent systems, control theory, wisdom computing, cloud computing, pervasive computing, and social computing are bringing smart computing and smart service systems to a newer dimension and improving mankind?s quality of life and living experience. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

This research project will study novel methods for designing sequential, non-myopic, online decision procedures for societal-scale cyber-physical systems such as public transit, emergency response systems, and power grid, forming the critical infrastructure of our communities. Online Optimization of these systems entails taking actions that consider the tightly integrated spatial, temporal, and human dimensions while accounting for uncertainty caused due to changes in the system and the environment. For example, emergency response management systems (ERM) operators must optimally dispatch ambulances and help trucks to respond to incidents while accounting for traffic pattern changes and road closures. Similarly, public transportation agencies operating electric vehicles must manage and schedule the vehicles considering the expected travel demand while deciding on charging schedules considering the overall grid load. The project's proposed approach focuses on designing a modular and reusable online decision-making pipeline that combines the advantages of online planning methods, such as Monte-Carlo Tree Search, with offline policy learning methods, such as reinforcement learning, promising to provide faster convergence and robustness to changes in the environment. The research activities of the proposed project are complemented by educational activities focusing on designing cloud-based teaching environments that can help students and operators with prerequisite domain and statistical knowledge to design, manage, and experiment with decision procedures. 

The societal-scale CPS that we study have spatial-temporal properties. The spatial aspect refers to the location-specific state variables such as traffic congestion, transportation demand, and the frequency with which incidents occur at a location. The temporal aspect refers to the dynamic nature of these systems---traffic congestion evolves over time. Non-myopic decisions entail selecting actions over time under uncertainty while accounting for future impact and demand for resources. The combined research and education efforts proposed in the project focus on answering the following critical questions for these systems - first, how do we solve the challenge of sampling future state/ environmental actions across a high-dimensional space while also tackling the challenge of non-stationarity? Second, how do we address the need for robust, fast non-myopic planning that also tackles potential non-stationarity? And third, how do we make it possible to engage non-computer science students and community partners with the solutions built using approaches pioneered in the project? The proposed approach involves investigating novel machine learning methods, such as normalizing flows for designing generative models and an innovative approach to design planning algorithms using a policy-augmented hybrid Monte-Carlo Tree Search approach. A significant effort of the project will focus on complementing fundamental research with the design of a cloud-based visual domain-specific modeling environment that can help explain the design, operation, and introspection of methods by using a block-based compositional approach. The work will be augmented with course modules and online tutorials accompanying the cloud-based environment. 

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Human blood clots kill an estimated 100,000 to 300,000 Americans each year. Current treatments rely on medications that break down clots, which can be combined with a surgical procedure that mechanically alters the clot. However, clot-busting medications and surgery are both linked to unintended adverse events. This project designs and studies miniature magnetic swimmers as a minimally invasive alternative to these treatments. These devices are millimeter-scale objects that have a helical shape and contain a small permanent magnet. A rotating magnetic field is used to remotely control the swimmers. The field makes the swimmer rotate and the helical shape converts the rotational movement into a propulsive force (much like a boat's propeller). The swimmers can be steered by changing the orientation of the applied magnetic field. They can precisely navigate within liquids along pre-defined 3D paths and could potentially navigate within the bloodstream toward a blood clot. The rotational movement can be used to abrade the clot and restore an appropriate blood flow. This project introduces new biomedical paradigms and investigates the robust high-speed tetherless manipulation of magnetic swimmers in complex, time-varying environments. It requires multiple swimmer designs, innovative controllers, and integrated, dynamic sensing modalities. The project is divided into three synergetic objectives. First, a fluid-dynamics model for the swimmer must be created and validated experimentally. This model will be used to optimize the swimmer's geometry. Secondly, a robust swimmer controller will be built. The controller must be able to compensate for the variable blood flow present in the arteries. An ultrasound scanner will be used to track the position of the swimmer and obtain useful information about its environment. For example, the velocity of the swimmer can be used to infer the blood flow velocity. Finally, the feasibility of several surgical procedures will be studied via ex-vivo experiments. 3D navigation together with the removal of human blood clots will be performed inside a human heart phantom. Several swimmers end effectors will also be tested. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The goal of this project is to advance the state of the art in autonomous Cyber-Physical Systems (CPS) by integrating tools from computer science and control theory. With the rise in deployment of autonomous CPS--from automotive to aerospace to robotic systems--there is a pressing need to verify and validate properties of these systems and thereby ensure their safe operation. The work will help establish the scientific basis for test and evaluation methods applicable to CPS, especially as they interact with other agents and the world in highly dynamic ways. This has the potential to inform the development and deployment of complex CPS in a variety of application domains: from (semi-) autonomous cars, to safety features in aviation, to robotic systems for industrial applications and space exploration. The appeal of dynamic CPS will be utilized to broaden participation in computing and engineering.

The vision of this project is to establish the scientific foundations for the verification and validation of highly dynamic safety-critical CPS operating in complex environments. The key novelty is a rigorous approach that leverages control barrier functions on the underlying nonlinear dynamics to provide guarantees of set invariance yielding: safety-critical abstractions on which to specify and verify desired properties, formal methods certifying system-level designs against those properties, and design rules that allow adaptation and machine learning to be integrated with control barrier functions thereby preserving system safety and performance specifications. Proof-of-concept experimental demonstrations will be performed on CPS that are autonomous, dynamic and safety-critical, e.g., robotic systems.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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