The objective of this CAREER project is to develop a theoretical and computational framework for the co-design of information and incentive mechanisms targeted at humans in Societal-Scale Cyber-Physical Systems (SCPS) in order to encourage efficient shared resource consumption while mitigating unintended consequences. The application focus is on intelligent transportation systems, a prototypical SCPS with humans in the loop, rapid technology adoption, and emerging mobility markets. CPS and internet of things (IoT) infrastructure is pervasive in the mobility landscape, allowing operators and intelligently augmented humans to make decisions rapidly as they interact with one another and engage with the system. Market mechanisms that support interactions occur on multiple timescales, are constrained by CPS dynamics, and are exposed to exogenous uncertainties, information asymmetries, and behavioral aspects of human decision-making. Through the design of algorithms with guarantees for learning in and shaping of SCPS, this project will address two fundamental components missing in the state-of-the-art: (i) models that capture the interactions and learning processes of different SCPS stakeholders, and (ii) certifiable algorithms with high-probability guarantees for the co-design of adaptive information and incentive mechanisms that achieve measurable improvement in system-level performance while ensuring individual-level quality of service and avoiding discriminatory policies. The validation approach uses a data-informed experimental platform with simulation and living lab components. The research agenda will aid in revising the design of operational mechanisms for both private and public CPS-enabled mobility platforms to include efficiency and measurable fairness as valued criteria. The proposed agenda includes an integrated research and education plan: (i) course development leveraging the experimental platform; (ii) undergrad research in which students aid in building out the experimental platform, and engage with municipal/industry partners; (iii) development of Girls In Research Labs (GIRLs), a week-long summer program in which middle school girls explore research labs across campus through hands-on projects.

Contributions to the science of CPS will be made through the study of intelligent infrastructure, with a special focus on behavior unique to human-in-the-loop CPS, and applications to multi-modal transportation systems. The technical plan is based on fundamental methods in decision sciences (control theory, game theory, behavioral economics, and mechanism design), statistics, and online learning. The research agenda is organized along two key thrusts: (i) algorithms for learning in SCPS and (ii) algorithms for shaping SCPS via incentives and information. The proposed tool set will enable analysis of multi-timescale decision-making of autonomous agents, including humans, coupled with CPS infrastructure in resource constrained environments, and will allow for the certifiable design of algorithms for learning and control (e.g., co-design of slow policy changes and real-time control). The modeling, synthesis, and validation approach will provide a principled, scientific basis for SCPS engineering design and operations, and supports CPS education by providing a platform for future engineers to discover realities associated with real-world implementation (e.g., socio-technical constraints). The unique perspective of co-designing information and incentives will also lead to new tools for modeling risk and uncertainties and thus, expose potentially new approaches to resilience in the engineering of CPS.

In the US, agricultural drainage infrastructure benefits >22.6 Mha of cropland and is valued at ~$100B. As a proportion of total croplands, drained croplands produce a disproportionately large amount of grain but also release a disproportionately large amount of eutrophying nutrients to aquatic ecosystems. Drainage systems include individually-owned field drains that depend on the function of community-owned main drains. Climate change and agricultural intensification are causing farmers to increase the extent and intensity of drainage leading to a pressing need to balance productivity, profitability, and environmental quality when making drainage decisions. Further, because drainage systems include individually-owned and community-owned drains, decision-making involves complex techno-economic social issues together with understanding biophysical processes and requires balancing the needs of individual farmers, drainage communities, and surrounding regions. This project will develop an integrated decision-making platform to facilitate community decision making for precise prediction and management of drainage effects on water flow, crop production, farm net returns, and nutrient loss. The platform data will be made possible by new agricultural sensors and robots, innovations in behavioral economics and analytics tools. Development of the drainage decision-making platform will be guided by farmer stakeholders?including, the Iowa and Illinois Drainage Districts Associations, a national-level agricultural drainage management coalition, and directly with farmers?forming a continuous learning environment across scientists and farmers that fosters adoption of new technologies and transfer of the research process to the next generation of scientists, engineers, and agricultural professionals.

The project will build upon a suite of biophysical and social science advances in multiple areas, including bioinspired robotic snake sensors, in-situ soil nutrient sensors, computational modeling, and socioeconomics. The snake sensors will navigate through agricultural drainage networks to generate a high spatial resolution data stream about flow rates and nitrate concentrations throughout the belowground network. The soil sensors will enable continuous monitoring of nitrate dynamics. Process-based ecohydrological models, subsurface water transport models, and multiple spatiotemporal sensor outputs will be integrated to obtain high-resolution information about distributions of water and nitrate. Biophysical scenario analyses will assist decision-making for different agricultural management scenarios to balance resource use efficiency, profitability, and environmental performance. Socioeconomic science innovations will be integrated by learning how current systems are managed in the context of various heterogeneities across individuals and drainage districts, such as demographics, farm size, and presence of wetlands, and how new information provided by the proposed infrastructure interacts with human incentives and choices and consequent policy making.

The goal of the project is to develop a methodology, supported by tools, that uses formal methods to gain clarity into the standards of network protocols and test the conformance of implementations to these standards. The work will be demonstrated on QUIC, a new complex protocol that is currently carrying about 10% of the internet traffic and is likely to carry much more of it in the near future. The standardization of QUIC is ongoing under the IETF. The software and the experimental results developed under this project will be stored in GitHub. The methodology itself will be widely disseminated by the usual means (publications, talks, etc.). The experimental results will be shared with the developers of QUIC implementations and the IETF. The project will be used to train graduate students who will be exposed to state-of-the-art techniques as well as collaboration between academia (UIC) and industry (Microsoft). The material of the course will be integrated in UIC classes. UIC is a Minority Serving Institution (MSI), Asian American and Native American Pacific Islander-Serving Institution (AANAPISI) and an Hispanic Serving Institution (HSI).

Network protocol standards are described by RFCs: English-language documents that provide extensive guidance for those implementing the protocol, but are nonetheless ambiguous and broadly open to interpretation. The primary mechanism for resolving these ambiguities and validating the protocol design is to produce multiple independent implementations, and to test these implementations for interoperability which often renders the RFCs obsolete and the implementations the de-facto standard. The goal of the project is to inject formal methods into the standardization process. The approach is based on a new methodology for specification-based testing that allows to rigorously specify and test complex Internet protocols. A primary objective is to demonstrate the practical value of formal specifications in the standardization process, and in particular to show that formal specifications have practical value beyond simply expressing the standard in a more rigorous way. This has a good chance to gain entry for formal methods in the Internet protocol development process, and thus ultimately to improve the reliability, security and maintainability of Internet services. The project will develop new methodologies, accompanied by tools, to apply lightweight formal methods in the Internet protocol domain. The work will also develop a formal specification of an emerging Internet standard of significant importance --- the QUIC secure transport protocol.

With the growing world population and diminishing agricultural lands, it becomes imperative to maximize crop yield by protecting crop health and mitigating against pests and diseases. Though there are decades-old practices still in place, there is also growing adoption of so-called precision agriculture solutions, which employ emerging technologies in sensing, automation, and analytics in daily farmland operations. As farmers gain real-time access to critical data (e.g., land and weather conditions) and can quickly share any untoward findings with others, farmland operations are morphing into full-fledged cyber-physical systems. To this end, this project seeks to develop, implement and evaluate a multi-robot agricultural information collection system that is autonomous, efficient and secure.

This project led by the University of North Florida (UNF) and supported by the University of Central Florida (UCF) has two main goals: (i) develop and implement novel information collection techniques for autonomous mobile robots that collect, store and share data in an efficient yet secure manner using blockchain,and (ii)and to train undergraduate and graduate students to conduct basic and applied research while closely working with local farmland partners in north-east Florida. Current technologies already use robots for agricultural purposes, but they typically have a high maintenance cost and do not necessarily consider issues related to security and data integrity. The primary objective is to design and deploy a set of autonomous robots that communicate wirelessly and navigate through planned paths in order to collect valuable data. This project will also consider the threat of security attacks by which collected data can be corrupted; seeking new distributed blockchain-based consensus protocols that mitigate the adversarial influence of such attacks. This project also contains a significant research and education component leveraging the leadership of UNF in the context of a primarily undergraduate institution (RUI). Being predominantly an undergraduate institution, there is a lack of opportunity for pursuing higher degrees in the Jacksonville area. This project aligns with an established Memorandum of Understanding (MoU) between UNF and UCF to provide a conduit for computing/engineering students to pursue M.S. degrees at UNF that feed seamlessly into Ph.D. programs at UCF. Students will benefit from the new robotics course to be developed at UNF and the ones being offered at UCF. Research progress will be showcased via technical workshops at both institutions to be held annually. Developed solutions are expected to transfer to other cyber-physical system applications, including search and rescue, patrolling, advanced manufacturing, among others. Most broadly, this project will raise awareness among today's teenagers and young adults of the impending agricultural crisis if worldwide food production falls even further behind meeting demands of an increasing global population.
 

Increasing wildfire costs---a reflection of climate variability and development within wildlands---drive calls for new national capabilities to manage wildfires. The great potential of unmanned aerial systems (UAS) has not yet been fully utilized in this domain due to the lack of holistic, resilient, flexible, and cost-effective monitoring protocols. This project will develop UAS-based fire management strategies to use autonomous unmanned aerial vehicles (UAVs) in an optimal, efficient, and safe way to assist the first responders during the fire detection, management, and evacuation stages. The project is a collaborative effort between Northern Arizona University (NAU), Georgia Institute of Technology (GaTech), Desert Research Institute (DRI), and the National Center for Atmospheric Research (NCAR). The team has established ongoing collaborations with the U.S. Forest Service (USFS) in Pacific Northwest Research Station, Kaibab National Forest (NF), and Arizona Department of Forestry and Fire Management to perform multiple field tests during the prescribed and managed fires. This proposal's objective is to develop an integrated framework satisfying unmet wildland fire management needs, with key advances in scientific and engineering methods by using a network of low-cost and small autonomous UAVs along with ground vehicles during different stages of fire management operations including: (i) early detection in remote and forest areas using autonomous UAVs; (ii) fast active geo-mapping of the fire heat map on flying drones; (iii) real-time video streaming of the fire spread; and (iv) finding optimal evacuation paths using autonomous UAVs to guide the ground vehicles and firefighters for fast and safe evacuation.

This project will advance the frontier of disaster management by developing: (i) an innovative drone-based forest fire detection and monitoring technology for rapid intervention in hard-to-access areas with minimal human intervention to protect firefighter lives; (ii) multi-level fire modeling to offer strategic, event-scale, and new on-board, low-computation tactics using fast fire mapping from UAVs; and (iii) a bounded reasoning-based planning mechanism where the UAVs identify the fastest and safest evacuation roads for firefighters and fire-trucks in highly dynamic and uncertain dangerous zones. The developed technologies will be translational to a broad range of applications such as disaster (flooding, fire, mud slides, terrorism) management, where quick search, surveillance, and responses are required with limited human interventions. This project will also contribute to future engineering curricula and pursue a substantial integration of research and education while also engaging female and underrepresented minority students, developing hands-on research experiments for K-12 students.

This project is in response to the NSF Cyber-Physical Systems 20-563 solicitation.

The gradual deployment of self-driving cars will inevitably lead to the emergence of a new important class of cyber-physical-human systems where autonomous vehicles interact with human-driven vehicles via on-board sensors or vehicle-to-vehicle communications. Reinforcement learning along with control theory can help meet the safety requirements for real-time decision making and Level 5 autonomy in self-driving vehicles. However, it is widely known that conventional reinforcement learning policies are vulnerable to adversarial or non-adversarial perturbations to their observations, similar to adversarial examples for classifiers and/or reward (packet) drops of the learning. Such issues are exacerbated by concerns of addressing resiliency as the use of open communication and control platforms for autonomy becomes essential, and as the industry continues to invest in such systems. Decision making mechanisms, designed to incorporate agility with the help of reinforcement learning, allow self-adaptation, self-healing, and self-optimization. This research will contribute and unify the body of knowledge of several diverse fields including reinforcement learning, security, automatic control, and transportation for resilient autonomy with humans-in-the-loop.

In this project, to counter action and observation manipulation as well as reward drops, the principal investigators will leverage proactive switching policies that aim (i) to provide robustness to adversarial inputs and reward drops in the closed-loop reinforcement learning mechanisms, (ii) to increase the cost of manipulation by deception, (iii) to limit the exposure of vulnerable actions and observations, and (iv) to provide stability, optimality, and robustness guarantees. Ultimately, the investigators will develop fundamental contributions to each of the above-mentioned fields and amalgamate these fields to provide a unique synthesis framework. The outcomes of this project will increase levels of confidence in autonomous technologies from ethical perspectives by providing an underpinning for curtailing accidents. The proposed framework can be extended to other key enablers of the global economy, including smart and connected cities, healthcare, and networked actions of smart systems while decreasing environmental pollution and minimizing the adverse environmental impacts on human health. The project will train the next generation of students from various levels, ages, and cultures through well-coordinated, level appropriate involvement in research and educational activities while providing a unique opportunity for the students to appreciate efficient, autonomous, and low-cost designs. This project will also contribute to future engineering curricula, pursue a substantial integration of research and education, and provide opportunities to engage students from the underrepresented group.
 

Rapid growth in additive manufacturing (AM) has improved the accessibility, customizability and affordability of making products using personal printers. Designs can be developed by consumers, if they have enough knowledge of mechanical design and 3D modeling, or they can be obtained from third parties. However, the process of translating a design to a program that can be successfully executed by a 3D printer often requires specialized domain knowledge that many end-users currently lack. In the meantime, lots of objects, which may be very similar or identical to what the non-technical user aims to design and print, have been produced by experts in industry and, hence, millions of proven part designs already exist. This research aims to fill the above-mentioned gap by developing a theoretically sound and practically deployable, domain-specific online search engine, called Srch3D, for 3D models. Srch3D will provide the non-technical end-users with a user-friendly solution to efficiently search for their components in a large repository of existing proven part designs.

The Internet of Things (IoT) is described as networks of small physical devices, embedded with sensors, software, and other technologies, that easily exchange data with other devices and systems over the Internet. The convergence of traditional technologies from wireless networking, control systems, and automation with miniaturization and low-powered devices contributed to the development of IoT, spurred on by strong demand and rapid growth in smart home automation and smart cities. Affordable interoperable IoT systems are increasingly ubiquitous in daily life. These IoT devices, working closely together, orchestrate a range of tasks, increasingly used for such activities as programmable personalized control of heating, cooling, and security in homes and offices. As these IoT devices become more capable, more computationally demanding tasks can be performed by these devices singly or in combination as a local distributed network bringing computing closer to the location where needed to improve responsiveness, i.e., at the edges of the Internet. The challenge is to ensure the highly capable, timely performance, seamless collective operation of IoT devices with edge computing and even cloud services as an efficient purposeful system.

This project studies the relationships between system resource utilization and energy efficiency in various edge and IoT systems in order to better understand how to optimize the key performance parameters of edge computing systems. This project explores mitigating the inefficiency in edge systems through a data-driven approach. Specifically, the primary research directions include: (1) analyzing the power inefficiency in different edge systems and develop a data-driven energy-aware framework for runtime edge and IoT applications, (2) tailoring the edge runtime framework including parts of data and control planes to reveal hidden dependencies, and (3) scaling and evaluating this framework and methodology in high-fidelity realistic test scenarios.
 

This Cyber-Physical Systems (CPS) grant will study smart tracking systems on scooters for ensuring safe and smooth interaction with other vehicles and pedestrians on the road. The smart system consists of inexpensive sensors, active sensing based estimation algorithms, and deep learning based robust image processing to enable trajectory tracking of all nearby vehicles on the road. If the danger of a scooter-vehicle collision is detected, an audio-visual alert is automatically provided to the car driver to make them aware of the presence of the scooter. The system also monitors the scooter rider's behavior, provides real-time feedback to improve rider compliance with traffic signals and sidewalk rules, and documents the information as a part of the rider's safety record. The key attractive features of the system are that it is inexpensive (< $500), is immediately useful on today's roads without requiring the vehicles on the road to be equipped with additional technology, and is potentially commercializable. The project contributes to the society by improving safety of micro-transportation systems, and broadens participation in computing via undergraduate research activities and promoting significant cross-disciplinary collaboration between faculty in engineering, computer science and human factors.