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.

High concentrations of energy use from fossil fuels can lead to poor air quality, resulting in adverse health effects as well as economic consequences. A prime example is found where large numbers of idling vehicles congregate (e.g., schools and hospital drop-off/pick-up zones), leading to microclimates of unhealthy air. Workers, such as valet parking attendants, can spend their entire workday in these microenvironments, and children passing through these zones can experience up to 60% higher levels of pollution than adults, because of their height. These vehicle-caused, poor-air-quality microclimates offer a compelling opportunity for communities to engage with emerging technologies to take ownership of their air and the behaviors that impact its quality. This project sociotechnical approach, called SmartAir, will synergistically integrate dynamic air-quality information with social-norm feedback to positively influence decisions that affect the well-being of vulnerable individuals working in or passing through polluted microenvironments. The feedback approach for decreasing idling mirrors the feedback provided by digital speed displays, which has been shown to positively influence driver behavior (reduced speeding) and thus reduce health-impacts of that behavior (reduced traffic accidents). The proposed pilot demonstrations will take place in Northern Utah, a region that periodically experiences the poorest air quality in the country. The project SmartAir employs a comprehensive community engagement approach ? from the development of the sensing and display technologies to cocreation of culturally sensitive messaging, cooperatively conducted pilot studies, and efficacy evaluation. SmartAir will produce novel technological and behavioral-science developments. First, this project will develop wearable, calibrated, low-cost air quality sensing nodes that will support members of smart and connected communities to minimize pollution exposure. Second, this project will enable the rapid integration of sensor measurements with local meteorological information and data-screening algorithms to dynamically provide feedback to individuals about idling behavior and to workers that seek to minimize pollution exposure. Third, the SmartAir system will be integrated into behavior-change experiments and the co-creation of community-crafted messaging to influence individual choices. Comprehensive involvement of the community partners will be critical to co-develop and pilot solutions to address poor air quality and ultimately ensure a highly scalable and sustainable system. The broader impacts of this work are multifold, including the following. SmartAir will serve as a framework for closing the loop between air quality measurements and individual decision making. It will also help drive institutional decisions that reduce worker pollutant exposure and improve worker performance, career longevity, and job satisfaction. Anonymized data will be made available to support numerous personal and community-driven needs, such as health-effects studies, anti-idling campaigns, school drop-off policies, and urban/traffic planning. Additionally, this project will have a substantial outreach effort that involves community members in message crafting, data collection, and interpretation.

The rapid growth of our aging population, nicknamed the "silver tsunami", causes both social and economic challenges. Without the appropriate tools to support independence, individuals age 65+ will overwhelm the limited resources of families, providers, and other supporting stakeholders nationwide. This project will engage diverse stakeholder groups to develop holistic technological and social solutions to address these challenges and promote the welfare, quality of life, autonomy, and dignity of older adults aging at home. The project develops technologies that can detect emergency, emergent, transitional and long-term changes in individuals? physical, social and cognitive states at times where prevention/early intervention can have the greatest benefits. These technologies will be paired novel social approaches that foster technology adoption by diverse populations. Overall, the approach will bring effective, cost-saving solutions at scale to many communities. The team will train students for interdisciplinary careers, cultivate the next generation of technology-savvy healthcare workers, and transfer the project findings to communities across the nation. This project will: (1) engage older adults, family and non-family caregivers, supporting institutions, and professionals within Suffolk County, Long Island, New York to identify health and social challenges that aging adults face; (2) design, develop, pilot test, and evaluate robust, secure, and affordable continuous health data collection and analysis solutions to automate health change detection, classification, and prediction (e.g., disease onset/progression/resolution); (3) develop social solutions and best practices that foster greater adoption of sensing technologies, share data effectively with stakeholders, and measure social determinants of health using a quantitative, data-driven approach; and (4) co-develop and co-evaluate the technical and social solutions with diverse community stakeholders---and educate/train students, residents, and providers end-to-end for technology assisted aging in place. The results will include novel technologies targeting challenges of older adults and best social practices in technology adoption to maximize the benefits for all stakeholders.

Approximately 91% of the world population lives in environments that do not currently meet air quality standards. In the United States (U.S.), the Clean Air Act of 1970 has resulted in air pollution concentrations dropping below national standards, meaning that most communities in the U.S. have cleaner air. However, clean air is not realized across all communities, especially in communities of color, where air quality can differ significantly. Further, regulatory air quality sensors that are sparsely deployed may not accurately detect the quality of air that residents breathe in their communities. With the availability of low-cost sensors and advancement of low-cost single-board computers and microcontrollers, this research aims to provide residents with an ability to accurately understand their air quality through the deployment of an Internet of Things (IoT) air quality sensor. We will meet with residents that have been affected by both redlining and nearby pollution sources to better understand how air quality affects their daily lives and what air quality information is most beneficial to them. In addition, the team will closely collaborate with partner school(s) to create K-12 curriculum for students to learn how to create their own air quality sensor, deploy it at their school, and make the air quality readings publicly available. In this research, we will combine the availability of low-cost particulate matter sensors with the accessibility of IoT compatible single-board computers and microcontrollers to enable publicly available fine-grained air quality information. To provide real-time access to the data, a prototype mobile application for both iOS and Android, along with a web dashboard, will be developed. To address common challenges of both power and connectivity, we will partner with PCs for People to deploy the sensors and provide connectivity through their existing infrastructure. An enclosure will be developed that ensures proper airflow, has low interference with wireless communication, and is modular to allow other sensing capabilities in the future. We will compare the findings from a test deployment of the sensors with regulatory sensors readings and share the results with the community and local officials. To ensure the sustainability of the project and provide an opportunity for it to expand, we will create an open-source Computer Science and Engineering curriculum in partnership with a local middle school and we will pilot a tech camp at our university.

Smart energy management is at the core of future smart cities, since energy profoundly impacts the city's livablity, workability and sustainability. Key building blocks for smart energy management are intelligent residential environments, generally termed smart homes. These homes will include a plethora of smart interconnected appliances, realized through the Internet of Things paradigm, which can improve residential energy efficiency by controlling the energy usage. This research aims at designing previously unexamined social behavioral models involved in the human interaction with both smart appliances and smart energy management systems. Based on these models, we make use of graph theory to design formal user models that enable algorithm design and optimization. In addition, we propose machine learning techniques to correlate social behavioral dimensions to quantitative metrics observable by smart devices as well as algorithms that use this correlation to refine the user model. The formal models are used to design social-behavioral aware efficient algorithms for energy optimization for individual smart homes, as well as for communities of multiple homes in a microgrid.