The U.S. delegation also included four members of U.S. private sector entities: CIO of the Southeast Pennsylvania Transportation Authority; Executive Director of the Colorado Smart Cities Alliance, a regional collaboration based in Denver; CEO of Marketplace.city, a public sector technology consulting firm; and a NYU professor and CEO of the urban design and data analytics firm, State of Place. The U.S. delegation thus consisted of federal and local government agencies, not-for-profit and for-profit enterprises, and a research university, all with expertise in technology integration for smart cities. In addition to meetings with Singaporean organizations and officials, delegation members attended sessions of the World Cities Summit—the largest urban planning and technology integration conference in the ASEAN region—and met with representatives of other ASEAN nations and commercial sector interests.
As the initial meeting between the U.S. and Singaporean governments on dedicated smart cities collaboration, the delegation accomplished the key objectives of establishing a channel for ongoing conversations and defining preliminary aspects of Singaporean and U.S. smart city ecosystems in order to help inform the structure of future U.S.-Singapore collaborations on smart cities, the development and integration of CET, and the commercialization of technologies through partnerships among business and industrial interests to the benefit of communities and residents of both Singapore and the United States.
NIST researchers Eugene Song and Kang Lee chaired and led the development of the IEEE 1451.0TM-2024 Standard for a Smart Transducer Interface for Sensors and Actuators - Common Functions, Communication Protocols, and Transducer Electronic Data Sheet (TEDS) Formats. A smart transducer is a smart sensor and/or actuator. The IEEE Standards Association published the 400-page 1451.0-2024 standard specification on June 26, 2024. This standard defines common functions of Internet of Things (IoT) sensor network components, network services, transducer services, and TEDS formats to help sensor networks achieve interoperability in network and transducer interfaces for about a dozen members of the IEEE 1451 family of standards. These network and transducer services are specified in structured message formats to simplify and standardize the communications of smart sensor and actuator, and device meta data and information. This standard also defines the universal unique identification (UUID), security framework, and time synchronization framework for IEEE 1451.0-based IoT sensor networks. IoT sensor network components (e.g., smart sensors and/or actuators, controllers, and devices) equipped with UUID, TEDS, and standardized interfaces and communication protocols can thus attain plug-and-play interoperability at the network and sensor interfaces, making IoT sensor network installation, maintenance, and upgrades easier and reducing the total life-cycle costs of sensor network systems.
Stakeholders of this standard include vendors, manufacturers, system integrators, and users of smart sensors and actuators, and controllers for Cyber-Physical Systems (CPS), IoT, and Industrial IoT (IIoT) applications, such as smart cities, smart grids, smart manufacturing, and smart transportation.
Sensors are ubiquitous and play a key role in IoT/IIoT applications. IoT sensors are connected to Internet/Ethernet to exchange sensor data with IoT applications, and made smarter or more intelligent using Artificial Intelligence (AI) technologies for self-description, self-identification, self-testing, and event-notification. Dr. Song presented capabilities of smart sensors including sensing, signal condition and conversion, data processing using AI algorithms, metadata, time synchronization, and network communication. He also overviewed interoperability challenges of smart sensors in IoT/IIoT applications, including:
Heterogeneity:
Wirelines and wireless connectivity.
Proprietary and standardized communication protocols.
Sensors provided by different vendors and manufacturers.
Lack of robust standards and standards harmonization.
Lack of interoperability tests and plugfests.
Lack of methodologies of interoperability modeling, testing, measurement and assessment, and software tools used to automatically measure and assess interoperability.
Then, he explained and analyzed the IEEE interoperability definition, and addressed an extension to the IEEE interoperability definition based on the standardized communication protocols in order to easily test, measure, and assess interoperability of smart sensors. Next, Song discussed what is interoperability testing, and pointed out that interoperability testing is an effective solution to overcome interoperability challenges and achieve and assure interoperability. After that, he presented two interoperability testing methods: device-to-device (D2D) and hardware-in-the-loop (HIL) simulation-based testing methods for smart sensors. Then, he presented two D2D test cases of IEEE C37.118 phasor measurement unit (PMU)-based, and IEEE 1451.0- and 1451.5-802.11-based smart sensors, and one HIL simulation-based test case of IEC 61850-9-2 merging unit (MU)-based smart sensors for real-time monitoring, protection, and control of electric power grids.
Finally, Song concluded by stating that interoperability testing and certification can be an effective solution to achieve and assure interoperability of smart sensors in IoT/IIoT applications, and that the presented interoperability testing methods and test cases not only test how to exchange the information, but also test how to use the information based on the standardized communication protocols. Smart sensor users, manufacturers, and interoperability testing laboratories can use these testing methods to develop interoperability testing systems to help achieve interoperability and, ultimately, plug-and-play interoperability of smart sensors.
At the 2024 Annual Modeling and Simulation Conference (ANNSIM) in Washington, DC, NIST researchers Serghei Drozdov and Mehdi Dadfarnia gave a 90-minute tutorial on using the NIST-developed open-sourced Python package, SimPROCESD, for the discrete-event simulation of discrete-part, multistage manufacturing systems. This conference attracts experts who showcase cutting edge research in modeling and simulation across various expertise domains.
The SimPROCESD software enables users to rapidly model and simulate part production in any manufacturing configuration that determines the flow of parts through manufacturing machinery and buffer stations to complete a job. The software also allows users to recreate the effects of different maintenance actions, from repairs triggered by Artificial Intelligence (AI)-driven condition-based predictive policies to time-based inspections and run-to-fail corrective work.
The NIST researchers used several examples to showcase the modularity of SimPROCESD's underlying design and its use for production planning and resource scheduling. They also presented a study on using the simulator to understand the risks and benefits of integrating AI-based condition monitoring systems with their maintenance practices. This tutorial represented work from a broader effort in NIST’s Industrial Artificial Intelligence Management and Metrology project, which develops domain-specific tools and methods to improve the effective use of AI systems and tools in industrial applications and to understand their financial and engineering risks and benefits.
NIST will hold a virtual workshop on Technical Language Processing on 24-25 September 2024. Online registration is $33.00 USD and is available via the workshop webpage. The two-day virtual event brings together industry, government, and academic stakeholders through the NIST Technical Language Processing Community of Interest (TLP COI), and it offers a valuable platform to connect, share insights, and explore the evolving landscape of TLP.
The workshop will include presentations and roundtable discussions to share experiences and results of TLP in various fields and explore the latest tools and methodologies in TLP applications with real-world examples from industry, government, and academic research. Event topics include Current Tools and Best Practices; Emerging Applications and Use Cases; and Methods and Philosophies for Quantifying Impacts.
Featured speakers include: Faez Ahmaed (Massachusetts Institute of Technology); Fazel Ansari (Institute of Management Science); Peter Chung (University of Maryland); and Rachael Sexton (NIST). Panelists include: Jamie Coble (University of Tennessee Knoxville); Joshua Gen (Logistics Management Institute); Nate Hertlein (Air Force Research Laboratory); Melinda Hodkiewicz (University of Western Australia); Manjish Naik (John Bean Technologies Corporation); and Roberto Sala (Università degli studi di Bergamo).
In an era where industry, artificial intelligence, and human-centered intelligent automation converge, staying ahead in communications and language processing is crucial. This year’s TLP COI meeting and workshop will emphasize:
Risk Awareness: Understanding and managing risks in TLP development and applications.
Accessibility: Making TLP tools and resources more accessible.
Longevity: Ensuring the sustainable development of TLP technologies.
Additional Key Topics include:
Introductory TLP: Exploring the fundamentals and differentiating TLP from Natural Language Processing.
Current TLP Landscape: State of the practice and best practices.
Risk Awareness: Integrating risk management in TLP development.
Data Sharing and Resources: Privacy-aware data sharing and available resources.
Needs, Gaps, and Applications: Identifying imminent needs and applications in various sectors.
Metrics and Measurement: Quantifying and qualifying the impact of TLP.
Exploring LLMs: Industrial and technical applications of large language models (LLMs).
Humans as Observers: Enhancing the interaction between operators and monitoring systems.
Standards Mining and Support: Accelerating model creation and validation from documentation.
For any inquiries about the workshop, please reach out to Dr. M. Sharp at michael.sharp@nist.gov.
The date and location for the 2025 MBE & QIF Summit have now been set – April 15-17, 2025, at the MxD (Manufacturing x Digital) headquarters in Chicago, Illinois.
In 2024, the MBE Summit in-person event returned and combined with the QIF Summit. The event was held from April 16-19, 2024, at MxD (Manufacturing x Digital) headquarters in Chicago, IL, and brought together experts from academia, government, and industry. The summit provided a platform for participants to discuss challenges, implementation issues, and lessons learned related to design, manufacturing, quality assurance, and sustainment of products and processes when a Model-Based approach is used.
Feedback from the 2024 event indicated that attendees enjoyed the location and size of the venue, leading to the decision to hold the 2025 event again at MxD Chicago on April 15-17, 2025. Suggestions for topics submitted through a survey were also well received, and the organizers are using this feedback to set the theme for the 2025 summit.
If you would like to attend the 2025 MBE & QIF Summit, April 15-17, 2025, for three days of technical presentations and networking opportunities,contactRosemary Astheimerto be notified when further details are available. 2024 event attendees will automatically be notified.
NIST researchers from the Communications Technology Laboratory's Smart Connected Systems Division recently hosted representatives from the Hawaii Technology Development Corporation (HTDC), one of 51 NIST Manufacturing Extension Partnership (MEP) centers located throughout the United States and Puerto Rico. The delegation was interested in CTL’s research and publications regarding cybersecurity for securing manufacturing environments, and artificial intelligence and machine learning (AI/ML) for smart manufacturing.
NIST researchers Timothy Zimmerman, Dr. M. Sharp, Michael Pease, and CheeYee Tang presented their findings on cybersecurity for operational technology (OT), focusing on applying NIST Guidance from NIST Special Publication (SP) 800-82r3 to various environments including manufacturing, continuous processes, and SCADA systems, all simulated within the laboratory. Additionally, they discussed their research on the increasing integration of AI/ML technologies in these settings.
Many questions were asked by the participants during the presentation, resulting in a lively discussion regarding how they can use NIST guidance to help their local Hawaiian manufacturers and MEP in general. Positive feedback was received from NIST MEP and HTDC in the post-tour follow-up. Many of the representatives in the tour group expressed interest in the work and publications produced by the projects and requested more information on the presentation.
Front-and-center of the tour was the Cybersecurity for Operational Technology laboratory and its three testbeds, representing three common types of OT control systems: discrete manufacturing systems, distributed control systems (DCS), and Supervisory Control and Data Acquisition (SCADA). The discrete manufacturing testbed is a collaboration between the Cybersecurity for OT project and the Industrial Artificial Intelligence Management and Metrology (IAIMM) project (both within CTL), enabling experiments and research data for both projects to be generated from the same testbed.
The DCS testbed is an improved version of the original “Process Control Testbed” described in NIST IR 8188, which emulates a chemical manufacturing process with components distributed over a large area, similar to an oil refinery.
The water system testbed emulates the operations of water utilities commonly found in cities and towns in the U.S. The testbed contains a SCADA system that uses devices common to the industry and provides a critical environment for cybersecurity research in the water sector.
The Manufacturing Extension Partnership (MEP) is part of the Commerce Department’s National Institute of Standards and Technology (NIST). The MEP National Network, whose mission is to strengthen and empower U.S. manufacturers, comprises the National Institute of Standards and Technology’s Manufacturing Extension Partnership (NIST MEP), the 51 MEP Centers located in all 50 states and Puerto Rico, the MEP Advisory Board, MEP Center boards, and the Foundation for Manufacturing Excellence, as well as over 1,440 trusted advisors and experts at approximately 460 MEP service locations, providing any U.S. manufacturer with access to resources they need to succeed. You can find more information about the NIST MEP at: https://www.nist.gov/mep.
The Cybersecurity for Operational Technologies project delivers cybersecurity implementation methodologies, best practices, metrics, and tools to enable OT owners and operators to implement cybersecurity capabilities while addressing the demanding performance, reliability, and safety requirements of OT systems. Our research provides OT owners and operators with a clear and effective roadmap for managing cybersecurity within their resource constraints, thereby reducing risk, and improving their overall cybersecurity posture. You can find more information regarding NIST's research and publications on cybersecurity for Operational Technology at: https://csrc.nist.gov/projects/operational-technology-security.
The Industrial Artificial Intelligence Management and Metrology (IAIMM) project develops and deploys measurement science to advance adoption and management of Artificial Intelligence (AI) systems in industrial environments to improve the productivity, resiliency, security, and the sustainability of manufacturing operations and supply chains. Our project aims to facilitate trust-based adoption of artificial intelligence in industrial tools like condition monitoring systems, while effectively capturing and conveying the value of using AI in these tools and their impacts through intuitive, risk-aware metrics and procedures. We enable better, more effective use of AI in industrial tools by developing deployment and evaluation best practices. You can find more information regarding NIST's research and publications on industrial artificial intelligence at: https://www.nist.gov/programs-projects/industrial-artificial-intelligence-management-and-metrology-iaimm.
