Cognitive workload refers to the level of mental effort put forth by an individual in response to a cognitive task. Unfortunately, no technology currently exists that can monitor an individual?s levels of cognitive workload in real-world environments using a seamless, reliable, and low-cost approach. We propose to fill this gap by using a novel magnetocardiography (MCG) system worn upon the subject?s chest to allow the sensor to collect the magnetic fields that are naturally emanated by the heart and associated with brain activity. This science is anticipated to greatly accelerate progress in such diverse disciplines as pediatric concussion recovery, pilot training, improved user-machine interfaces, injury prevention in construction environments, increased human performance in risky missions, and improved education outcomes. In addition to advances in basic science, the proposed research is expected to be of significant interest to students and the public. Through targeting interdisciplinary education and diverse recruitment, we intend to expose new audiences to STEM concepts via workshops and family-friendly outings. 

The proposed MCG sensor is smartly integrated in a Cyber-Physical System (CPS) with two inter-connected loops: (a) a human-in-the-loop that addresses changes in the thresholds of different cognitive states as a function of time, and (b) a non-human-in-the-loop that adapts the system?s algorithmic and hardware components for high-accuracy classification of cognitive workload with minimum resource usage. Our goals are to: (1) Build a knowledgebase concerning the impact of hardware/algorithmic advances upon MCG sensor performance in real-world settings. (2) Explore the classification of cognitive workload from MCG data and close the loop with the wearer for dynamic calibrations that address the time-varying thresholds of cognitive states. (3) Ensure operability in dynamic real-world settings and close the loop between the cyber and physical sides for minimal resource usage. (4) Validate the CPS within the framework of measuring cognitive workload for children with concussion. Without loss of generality, we select this population given the immense clinical potential: the effects of cognitive activity on pediatric concussion recovery are currently unknown, largely due to the difficulties in quantifying cognitive activity workload.

Increases in temperatures and drought duration and intensity due to climate change, together with the expansion of wildlife-urban interfaces, has dramatically increased the frequency and intensity of forest fires, and has had devastating effects on lives, property, and the environment. To address this challenge, this project?s goal is to design a network of airborne drones and wireless sensors that can aid in initial wildfire localization and mapping, near-term prediction of fire progression, and providing communications support for firefighting personnel on the ground. Two key aspects differentiate the system from prior work: (1) It leverages and subsequently updates detailed three-dimensional models of the environment, including the effects of fuel type and moisture state, terrain, and atmospheric/wind conditions, in order to provide the most timely and accurate predictions of fire behavior possible, and (2) It adapts to hazardous and rapidly changing conditions, optimally balancing the need for wide-area coverage and maintaining communication links with personnel in remote locations. The science and engineering developed under this project can be adapted to many applications beyond wildfires including structural fires in urban and suburban settings, natural or man-made emergencies involving radiation or airborne chemical leaks, "dirty bombs" that release chemical or biological agents, or tracking highly localized atmospheric conditions surrounding imminent or on-going extreme weather events.

The system developed under this project will enable more rapid localization and situational awareness of wildfires at their earliest stages, better predictions of both local, near-term and event-scale behavior, better situational awareness and coordination of personnel and resources, and increased safety for fire fighters on the ground. Models ranging from simple algebraic relationships based on wind velocity to more complex time-dependent coupled fluid dynamics-fire physics models will be used to anticipate fire behavior. These models are hampered by stochastic processes such as the lofting of burning embers to ignite new fires, that cause errors to grow rapidly with time. This project is focused on closing the loop using sensor data provided by airborne drones and ground-based sensors (GBS). The models inform the sensing by anticipating rapid growth of problematic phenomena, and the subsequent sensing updates the models, providing local wind and spot fire locations. Closing this loop as quickly as possible is critical to mitigating the fire?s impact. The system we propose integrates advanced fire modeling tools with mobile drones, wireless GBS, and high-level human interaction for both the initial attack of a wildfire event and subsequent on-going support.

Today, operators of cellular networks and electricity grids measure large volumes of data, which can provide rich insights into city-wide mobility and congestion patterns. Sharing such real-time societal trends with independent, external entities, such as a taxi fleet operator, can enhance city-scale resource allocation and control tasks, such as electric taxi routing and battery storage optimization. However, the owner of a rich time series and an external control authority must communicate across a data boundary, which limits the scope and volume of data they can share. This project will develop novel algorithms and systems to jointly compress, anonymize, and price rich time series data in a way that only shares minimal, task-relevant data across organizational boundaries. By emphasizing communication efficiency, the developed algorithms will incentivize data sharing and collaboration in future smart cities.

The key motivation of this work is that today's representations of time series data are designed independently of an ultimate control task, which often causes unnecessary temporal features to be sent, private features to be revealed, and the most salient trends to be under-valued. Accordingly, this project will develop a unified approach to co-design succinct, private representations of rich time series data along with an ultimate control task. Here, co-design means that the forecast representation is learned within the broader context of a control objective while accounting for bandwidth constraints, privacy, and economic costs and incentives for data processing. The algorithms will compute a controller's sensitivity to prediction errors, which can arise from data compression, forecast uncertainty, as well as artificial noise injected by modern privacy tools. Crucially, the controller's sensitivity will in turn be relayed to a network operator to guide its optimization and learning (e.g., co-design) of a concise, task-relevant forecast representation that masks private attributes and naturally prices temporal features by their importance to control. The research will, for example, enable operators to flexibly use the same underlying cell demand data to emphasize peak-hour variability for taxi routing, while seamlessly delivering fine-grained throughput forecasts to a mobile video streaming company without revealing private user mobility. Finally, the case studies in this project will be integrated into courses on learning-based control at UT Austin and Cornell. Broader impacts also include outreach and inclusion efforts to engage students from groups that have historically been under-represented in STEM fields.
 

The proposed decentralized/distributed control and optimization for the critical cyber-physical networked infrastructures (CPNI) will improve the robustness, security and resiliency of the electric distribution grid, which directly impacts the life of citizens and national economy. The proposed control and optimization architectures are flexible, adapt to changing operating scenarios, respond quickly and accurately, provide better scalability and robustness, and safely operate the system even when pushed towards the edges by leveraging massive sensor data, distributed computation, and edge computing. The algorithms and platform will be released open source and royalty-free and the project team will work with industry members and researchers for wider usage of the developed algorithms for other CPNI. Developed artifacts as part of the proposed work will be integrated in existing undergraduate and graduate related courses. Undergraduate students will be engaged in research through supplements and underrepresented and pre-engineering students will be engaged through existing outreach activities at home institutions including Imagine U program and 4-H Teens summer camp programs and the Pacific Northwest Louis Stokes Alliance for Minority Participations. Additionally, project team plans to organize a workshop in the third year to demonstrate the fundamental concepts and applications of the proposed control and optimization architecture to advance CPNI. Developed solutions can be extended for range of applications in multiple CPNIs beyond use cases discussed in the proposed work.

While the proposed control architecture with edge computing offer great potential; coordinating decentralized control and optimization is extremely challenging due to variable network and computational delays, several interleavings of message arrivals, disparate failure modes of components, and cyber security threats leading to several fundamental theoretical problems. Proposed work offers number of novel solutions including (a) adaptive and delay-aware control algorithms, (b) Predictive control and distributed optimization with realistic cyber-physical constraints, (c) threat sharing, data-driven detection and mitigation for cyber security, (d) coordination and management of computing nodes, (e) knowledge learning and sharing. Proposed solutions will be a step towards advancing fundamentals in CPNI and in engineering next generation CPNI. The proposed work also aims to use high fidelity testbed to evaluate developed algorithms and tools for specific CPNI: electric distribution grid.
 

This award will support a conference for the development of a roadmap for the control community that identifies societal-scale challenges and research paths for addressing them. The roadmap will seek to identify key societal drivers, emerging technological trends, and outline methodologies that squarely address the fundamental challenges by embracing these drivers and leveraging advances in enabling technologies. By doing so, the conference will underscore scientific challenges that the community should pursue, and investigate workforce education and training curricula, with the grand goal of a solid impact by our community on the societal-scale challenges. The funding will be used for travel support for attendees from the United States to participate in the conference and contribute to these deliberations. The 2022 IEEE CSS Workshop on Control for Societal-Scale Challenges (CSS-CSC) is planned to be a decennial event organized by the global control community. The 2022 IEEE CSS Workshop on Control for Societal-Scale Challenges will be an in-person event and will be held in Stockholm, Sweden, June 17-18, 2022. This is intended to be the first of a decennial series of such events, and will be cosponsored by the IEEE Control Systems Society. The main goal of this workshop is to produce a scientific roadmap for the future of our discipline, Control for Societal-Scale Challenges: Roadmap 2030. This roadmap will include themes such as climate change, sharing economy, public health, neuro-engineering, robotics and automation, cyber-physical human systems, smart societies, 6G networks, and Artificial Intelligence. 

The CSS-CSC workshop is being organized by an international committee of scientific excellence, leaders in the control community. Many of the attendees are of equally well-known stature, if not even more so, and have made seminal contributions to the community over the last four decades. This workshop will contribute to the fundamental goal of identifying, underscoring, and advancing potential contributions from the control community for addressing societal-scale challenges that are emerging in the 21st century. This conference is expected to pave the way for a series of such events, with a similar large scope, to occur once every decade. The entire focus of this workshop is in achieving a broader impact on all walks of society including energy, transportation, healthcare, robotics, manufacturing, and overall quality of life. The technologies, the challenges, the opportunities, the methodologies, the tools, the validation testbeds, education, training and retraining of workforce, are the key elements that will be explored in the conference. All in all, every aspect of the proposed conference is intended to achieve a broader impact on the scientific community, and in fact the entire society, as we advance into the 21st century.

This project seeks to develop design methodologies for the synthesis of cyber-physical systems (CPS) that verifiably satisfy given safety and performance requirements when an unknown set of system components is compromised. The need for such design methodologies is exemplified by recent intrusions into nuclear facilities and ransomware attacks on municipal governments, in which adversaries found weak points in cyber defenses that were leveraged to control safety-critical physical infrastructures. The research plan is grounded on two application scenarios: (i) a group of unmanned vehicles that must complete high-level task objectives while avoiding collisions in the presence of false and malicious sensor and control inputs, and (ii) a smart building in which IoT apps send malicious commands to the building HVAC and other safety-critical systems.

The PI will develop algorithms to compute control policies in the presence of attacks that inject arbitrary sensor measurements or control signals, disrupt availability of sensor or control messages, and/or modify controller set points. The first research thrust will investigate and develop control strategies for safety and reachability of nonlinear systems under attack by extending the notions of control barrier and control Lyapunov functions to adversarial settings. The second thrust will investigate resilient synthesis of more complex task specifications using the control algorithms of thrust one as building blocks. The PI will develop novel approaches to model adversarial cyber-physical interactions as stochastic games by developing resilient finite-state abstractions of nonlinear systems. Finite-state control policies will be developed by approximating the game solutions. This thrust will investigate contract-based decomposition algorithms for solving the games in a distributed system with multiple (potentially malicious) decision-making agents. Each thrust of the project will be validated through experimentation and testing on two custom platforms, namely, a multi-robot testbed and a smart building simulation framework. This project will result in models and algorithms to improve safety, performance, and security of CPS including connected and autonomous vehicles, industrial control systems, intelligent traffic management systems, medical devices, and manufacturing CPS. The PI will develop ?serious games? to enhance public interest while providing insight into human decision-making. Algorithms for secure control developed in the project will be experimented on by undergraduate capstone students under the supervision of the PI?s graduate students.

Emerging mobility systems, e.g., connected and automated vehicles and shared mobility, provide the most intriguing opportunity for enabling users to better monitor transportation network conditions and make better decisions for improving safety and transportation efficiency. However, different levels of vehicle automation in the transportation network can significantly alter transportation efficiency metrics (travel times, energy, environmental impact). Moreover, we anticipate that efficient transportation might alter human travel behavior causing rebound effects, e.g., by improving efficiency, travel cost is decreased, hence willingness-to-travel is increased. The latter would increase overall vehicle miles traveled, which in turn might negate the benefits in terms of energy and travel time. The project will consolidate emerging mobility systems and modes with real-world data and processed information leading to an equitable transportation system with broad economic, environmental, and societal benefits. We expect the outcome of this project to enhance our understanding of the rebound effects, changes in travel demand and capacity, human reception, adoption, and use of emerging mobility systems. 

The outcome of this research will deliver an online learning framework that will aim at distributing travel demand in a given transportation network resulting in a socially-optimal mobility system that travelers would be willing to accept. A ?socially-optimal mobility system? is defined as a mobility system that (1) is efficient (in terms of energy consumption and travel time), (2) does not cause rebound effects, and (3) ensures equity in transportation. The framework will establish new approaches in optimally controlling cyber-physical systems by merging learning and control approaches. It includes the development of new methods to enhance accessibility, safety, and equity in transportation and travelers? acceptance. In the context of the proposed framework, a ?social planner? faces the problem of aggregating the preferences of the travelers into a collective, system-wide decision when the private information of the travelers is not publicly known. Mechanism design theory will be used to derive the optimal routes and the selection of a transportation mode for all travelers so as to maximize accessibility, safety, and equity in transportation and travelers? acceptance. Online learning algorithms for contextual bandit problems will be developed to identify traveler preferences and to determine how they would respond to the social planner?s recommendations on routing and selection of a transportation mode.

Active user participation in large-scale infrastructure systems, while presenting unprecedented opportunities, also poses significant challenges for the operator. One such example is electric power distribution systems, where the massive integration of distributed energy resources (DERs) and flexible loads motivates new decision-making paradigms via demand response through user engagement. This project introduces a novel approach for intelligent decision making in power distribution systems to efficiently leverage flexible demand commitments in highly uncertain and stochastic environments. The project goals are to (1) develop analytics required to enable actionable demand-side flexibility from several small consumers by adequately representing their constraints regarding electricity usage and their interactions with the system and the energy provider; and (2) develop a prototype for demand-side coordination using an open-source testbed for distribution systems management and evaluate the proposed algorithms with real-world utility data. Successful completion of this project will provide solutions to adaptive and smart infrastructure systems in which passive users turn into active participants. For the demand response focus here, this project will enable high levels of penetration of flexible loads and DERs economically through the transformation of grid operation from load following to supply following. The results from this project will provide valuable guidance to policymakers and electric utilities in managing aggregator-driven markets. 

The central aim of this proposal is to enable the demand-side participation of many small customers in a distribution grid and solve for an interface between customers and an energy provider. The proposed architecture follows a two-level structure: a home energy management system (HEMS) providing a home-level interaction between the consumer and the HEMS, and a feeder-level interaction between the HEMS and the demand-response provider. Research along two thrusts will be proposed: (1) learning-based control to achieve home-level flexibility upon learning and incorporating customer constraints and preferences into the decision-making process; and (2) game-theoretic constructs to aggregate and coordinate the home-level flexibility at the network-level in a constrained environment with unknown customer utility functions. Technical innovations at the HEMS-customer interface will include automata learning-based algorithms used by HEMS to learn customers? temporally evolving energy usage constraints, and reinforcement learning algorithms to satisfy temporal constraints while optimizing the cost of electricity consumption. At the provider-HEMS interface, technical innovations will include a new mean field based model of customers that allows the provider to interact with only a few customer classes, and a Stackelberg game formulation that explicitly incorporates network congestion constraints.

This research examines the scientific foundations for modeling security and privacy trade-offs in cyber-physical systems, focusing in particular on settings where privacy-protection technologies might be abused by malicious parties to hide their attacks. The goal is to provide both security and privacy guarantees for a variety of cyber-physical systems including intelligent transportation systems, smart energy, and autonomous vehicles.

Privacy and security in cyber-physical systems have been studied independently before, but often they have not been addressed jointly. This project will study privacy-protection mechanisms such as differential privacy, and explore how using such mechanisms can affect the state-of-art integrity and attack-detection mechanisms. The project will also develop novel defenses including: 1) Identifying fundamental trade-offs between privacy and security based theoretical analyses of privacy, control theory, and optimization methods, with applications such as traffic-density estimation and smart grids; 2) incorporating game-theoretic considerations in analyzing adversarial strategies; and 3) Proposing new privacy-preserving techniques applicable in cyber-physical systems and beyond.

This project focuses on tackling the security and privacy of Cyber-Physical Systems (CPS) by integrating the theory and best practices from the information security community as well as practical approaches from the control theory community. The first part of the project focuses on security and protection of cyber-physical critical infrastructures such as the power grid, water distribution networks, and transportation networks against computer attacks in order to prevent disruptions that may cause loss of service, infrastructure damage or even loss of life. The second part of the project focuses on privacy of CPS and proposes new algorithms to deal with the unprecedented levels of data collection granularity of physical human activity. The work in these two parts focuses on the integration of practical control theory concepts into computer security solutions. In particular, in the last decade, the control theory community has proposed fundamental advances in CPS security; in parallel, the computer security community has also achieved significant advances in practical implementation aspects for CPS security and privacy. While both of these fields have made significant progress independently, there is still a large language and conceptual barrier between the two fields, and as a result, computer security experts have developed a parallel and independent research agenda from control theory researchers. In order to design future CPS security and privacy mechanisms, the two communities need to come closer together and leverage the insights that each has developed. This project attempts to facilitate the integration of these two communities by leveraging the physical properties of the system under control in two research problems: (1) Physics-based CPS security; and (2) Physics-based CPS privacy.

Physics-based CPS security leverages the time series from sensor and control signals to detect deviations from expected operation. This is a growing area of research in both security and control theory venues, although there are several open problems in this space. This proposal tackles some of these open problems including the definition of new evaluation metrics that capture the unique operational properties of control systems, the consistent evaluation of different proposals for models and anomaly detection tests, and the development of new industrial control protocol parsers. Physics-based CPS privacy focuses on how to guide the implementation of general privacy recommendations like the Fair Information Practice principles into cyber-physical systems, leveraging the fact that these physical systems often have an objective to achieve, and this objective depends on the data-handling policies of the operator. The project focuses on investigating the trade-off between privacy and control performance and developing tools to guide how data minimization, data delays, and data retention should be implemented.