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Communications Technology Laboratory / Smart Connected Systems Division

Transformational Networks and Services Group

Projects/Programs

Displaying 1 - 12 of 12
Operating Envelope Specification supports automated driving safety measurement, key to testing and public acceptance of automated vehicles

Automated Vehicles and AV Communications

Ongoing
Automated vehicles (AVs), or Automated Driving Systems-equipped (ADS-equipped) vehicles, have great potential to improve our lives by freeing us from the stress of driving and reducing accidents caused by distractions or poor judgement. Connected, reliable, and safe AVs will spark significant

5G/6G Core Networks and Services

Ongoing
The main tasks of this program are: Task 1: 6G Core Network Architecture: I nvestigating AI-native architectures that not just execute AI/ML-based network functions but also support the creation, management, cooperation, measurement, and evolution of these functions. Task 2: Key Technologies Task 2
5G Core Network Testbed Project

5G Core Networks Testbed

Ongoing
This project, as part of our 6G Core and Edge Networks and Services Project, is building a 5G/6G Core Network Testbed— which we refer to as OpenCoreNet—to support research on sixth generation (6G) networks and to explore how 6G can best improve industry applications. OpenCoreNet focuses on core

Edge AI

Ongoing
This project: Develops edge learning and edge-enabled collaborative learning algorithms. Develops measurement methods for testing the performance and robustness of edge-learned, and more generally collaboratively learned, AI/ML models. Collaborates with other projects (e.g., 5G/6G Core Network
Information Centric Networking Program Image

Information Centric Networking Program

Ongoing
Today the Internet relies on content distribution network overlays and caching to deliver most of its traffic. It is not clear that this architecture can continue to scale to meet data projections of the next decade and beyond. Current research in information-centric networking proposes a new

Internet Infrastructure Protection

Ongoing
In the FY 2016 ITL staff made significant contributions in the design, standardization, test and measurement of technologies to improve the security and robustness of the Internet’s global routing protocol BGP. NIST staff are key contributors to Internet Engineering Task Force (IETF) standards to

Mobile Cloud Computing

Ongoing
Design robustness using formal language This effort develops a formal specification using the π-calculus to define a virtual device representation. It also describes a way to compose multiple virtual devices representing physical devices available on the network to build a composite virtual
autonomous vehicles view from above

NIST Automated Vehicles Program

Ongoing
Measurement science and standards are needed to support the safe and predictable operation of future automated vehicles (AVs), which have great potential to significantly impact our daily lives and improve the competitiveness of our economy. A FY22 NIST Strategic and Emerging Research Initiatives
Logo that says NIST Cloud Computing Program and has a half blue cloud

NIST Cloud Computing Program - NCCP

Ongoing
Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction

Quantum Optical Networks

Ongoing
The program's technical research areas are: Architecture research for Quantum Optical Networks and integration with classical networks Management (label, identify, track) and Control Plane (signal and route optical paths) Software Stacks Performance monitoring for end-to-end Quality of Entanglement
Trustworthy Intelligent Networks Project

Trustworthy Intelligent Networks

Ongoing
Our current research efforts include: The application of AI/ML techniques to detect abuse of the Domain Name System (DNS). The development of measurement techniques to characterize the robustness of AI/ML approaches to botnet detection. The application of AI/ML techniques to detect anomalies in the