The Global Community Technology Challenge (GCTC) is a U.S. smart cities and communities program led by the Smart Connected Systems Division of the National Institute of Standards and Technology (NIST). The GCTC was formed in 2014 as a partnership of cities and communities, local and state government agencies, business enterprises, non-governmental organizations, universities, and research institutes dedicated to improving the urban environment through the integration of digital technologies.
The GCTC program has recently published its first Strategic Plan (2024-2026), which describes a 3-year program of research and development and reaffirms a continuing collaboration between the federal smart cities program led by NIST and communities, cities, and regions across the country. The GCTC Strategic Plan is built on three key goals:
Establish a research-based, scientific foundation for the NIST Smart Cities Infrastructure program, the GCTC, and the broader smart cities community.
Broaden the scope and agenda for smart cities to address current challenges and achieve the equitable distribution of outcomes for community residents, businesses, and organizations.
Enhance the national public-private partnership of smart city programs, research institutions, private sector enterprises, and the next generation of community leaders, scientists, and researchers.
The GCTC is organized into twelve Technology Sectors, corresponding with city infrastructure systems, services, and programs that can benefit from the integration of advanced digital technologies to improve overall quality of life for community residents.
NIST is seeking public comment on the new strategy and will review all stakeholder comments, with a final version of this document to be published this Spring after modifications are made to address the received feedback. The GCTC Strategic Plan is available as a free download from the NIST Special Publication library as an Initial Public Draft and available for public comment until 29 February 2024. Your input is requested! Instructions for submitting comments to GCTCinfo@nist.gov on the Strategic Plan appear on page i of the document, which may be accessed directly at: https://doi.org/10.6028/NIST.SP.1900-207.ipd
Digital Twins represent a growing global marketplace valued at over $8.6 billion in 2022 and forecasted to reach $138 billion by 2030. The NIST internal Strategic and Emerging Research Initiative (SERI) program has provided Fiscal Year 2024 (FY24) funding to perform a research study to identify issues and opportunities in measurement science, trustworthiness, innovation, and standards needed to support Digital Twin ecosystems. Michael Pease from the Smart Connected Systems Division has been selected as a co-lead of this NIST-wide team, which includes representatives from different NIST Laboratories, to investigate the research opportunities for Digital Twins in areas such as Manufacturing, Construction, Smart Cities, Healthcare, Business, Communications, Energy, and Climate. This effort will help ensure that NIST is focusing on the most impactful research to support this growing marketplace and develop the standards, guidelines, reference data, frameworks, and metrology required to enable trust, security, and interoperability for Digital Twins.
The goal of this study is to engage industry stakeholders and develop a detailed overview of digital twins and application areas, industry needs and technical barriers, and the standards landscape including existing efforts, gaps, and needs. This information will inform recommendations for research priorities for NIST across different labs and divisions.
The project kickoff with the NIST Associate Director for Laboratory Programs was held on December 7, 2023, and the project team is currently coordinating with members from the different laboratories to reach out to key stakeholders, identify existing standards efforts, establish focus groups, and begin organizing NIST-run workshops to be hosted later this year. If you would like additional information on this effort, or if you believe that your research may be aligned with this study, please reach out to either Michael Pease (michael.pease@nist.gov) or Simon Frechette (simon.frechette@nist.gov).
In the recently published Research Handbook on AI and Communications, NIST researcher Dr. Edward Griffor, with academic collaborators, describes the importance of the NIST Cyber-Physical Systems (IoT) Framework for artificial intelligence (AI) in communications. The NIST contribution is described in the emerging field of studies on communication of, by, and with AI. The book chapter provides a comprehensive analysis of the complex intersections between AI and communication. In this contribution, the CPS Framework Aspects and Concerns are used to analyze the dimension of AI communications risk as it relates to business intelligence.
AI has produced impactful results across a multitude of domains. Business solutions that previously seemed impossible are now made possible based on AI-enabled components, whose use is often imperative to the overall success of business processes. However, leveraging AI is not trivial. Griffor argues, “Given the complexity of AI components and their behavior, communication is a major hurdle among stakeholders with different backgrounds and goals.” Stakeholders may have their own set of concerns and requirements, vocabulary can vary depending on each stakeholder’s domain of expertise, and each group likely has its own goals that may conflict with other groups’ goals. For example, public relations experts may want to promote transparency surrounding decisions of AI-enabled systems and business processes. However, cybersecurity experts may argue that excessive transparency would threaten security. Then Griffor and his co-authors explain how to manage the multi-layered business processes involving AI-enabled systems, integrating requirements using the NIST CPS Framework aspects and concerns as the overarching structure of requirements.
While the NIST CPS framework was explicitly conceived to guide the design and development of CPS and IoT systems, Griffor points out that “the underlying approach can be used to overcome the challenges that emerge from the use of AI components in AI-enabled systems and business processes - especially “black-box” AI components.” The book chapter illustrates this methodology with an example of a Fintech company that wants to develop requirements for business processes related to processing credit card applications in which the business processes use AI, where the goal is to minimize bias and ensure the fairness of the credit card application assessment process.
On January 18, 2024, NIST welcomed Dr. Paul Perrone, Founder and CEO of Perrone Robotics, Inc., to give a NIST Colloquium presentation on “Autonomous Vehicles: The World’s Most Complicated Technical Problem.” During his visit, Perrone had discussions with representatives of NIST’s Automated Vehicle program, including David Wollman, Ed Griffor, Tom Roth, and Prem Rachakonda, and with other NIST officials including Laurie Locascio, NIST Director and Under Secretary of Commerce for Standards and Technology, and Chuck Romaine, NIST Associate Director for Laboratory Programs.
Perrone’s presentation made the case that the overwhelming challenges and difficulties of developing autonomous vehicles (AVs) made it the world’s most complicated technical problem, based on Perrone’s extensive experience in the broad field of automation. Perrone covered a comprehensive list of AV complexities, including hardware and software adaptation, expanding and evolving operational design domains, artificial intelligence (AI) and machine learning, human interactions with AVs, and edge cases. He also provided mitigations and a potential trajectory for successful AV development including multiple time phases and well-defined control zones, along with advancements in use of AI, increased standardization, and vehicle-to-infrastructure communications. The presentation was followed by a lively question and answer session, including on human factors considerations and challenges.
The increased adoption of energy technologies like smart meters, solar panels, and battery storage has led to a rise in electricity pricing plans with costs that vary based on the time of day or energy usage. The impact of these pricing plans on both the customer (in terms of managing costs) and the electric grid (in terms of reducing peak energy usage) can be evaluated using simulation software. However, most of these simulations do not consider differences between residential buildings or their residents that affect energy usage. As an example, a neighborhood of older homes with poor insulation will require more heating. Or, neighborhoods with different median income levels can have installed home appliances with different energy efficiencies. This makes it difficult to evaluate how electricity pricing plans will affect different types of residential customers.
Researchers from Santa Clara University, in collaboration with NIST, created simulations that contain multiple residential building models representative of different residential customers. These models use income level and climate zone data to help determine the size, insulation, number of windows, number of appliances, energy usage, and other home characteristics. An approach called co-simulation was used to combine the building models with an electric distribution grid simulation. The research team implemented different electricity tariffs in the simulation to explore the impact of those tariffs on different types of residential customers. The results show that electricity tariffs have different impacts on residential customers dependent on their income levels. When comparing electricity tariffs, the fairness to customers across different income levels is a factor that should be considered.
