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NOBEL PRIZE 2026

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NSFDirectorate for Technology, Innovation, and Partnerships

Collaborative Research: VINES: Track 2: NSF-BF: Mission Critical Semantic Communications in NextG Networks for Smart Manufacturing

Active
Institution
Stevens Institute of Technology, NJ
Principal investigator
Victor Lawrence
Funding
$900K
Duration
2026–2029
Open portfolio

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Still being scored

Abstract

This project will develop new communication technologies to support next-generation (NextG) wireless networks for smart manufacturing and other mission-critical applications. Modern industrial environments rely on connected robots, sensors, and artificial intelligence (AI) to improve productivity, safety, and efficiency. However, current wireless systems struggle to meet these demands due to high data volumes, latency constraints, and limited understanding of complex physical environments. This project addresses these challenges through semantic communications, which focuses on transmitting the meaning of information rather than raw data. This approach enables real-time situational awareness and predictive decision-making, improving reliability, reducing downtime, and enhancing workplace safety. This international collaborative project between the United States and Finland will strengthen U.S. leadership in NextG technologies, support advanced manufacturing, and train a diverse workforce through interdisciplinary research. The project will establish a unified framework integrating communication, sensing, and computation using semantic communications and digital twin technologies. It will develop task-oriented resource management, AI-driven multimodal situation awareness, semantic scene representations, and adaptive coding to optimize network performance. These components will form an end-to-end system enabling real-time interaction among sensors, robots, and edge/cloud intelligence. The system will be validated through staged demonstrations, from laboratory testbeds to realistic smart manufacturing environments and field trials. Expected outcomes include new theories, algorithms, architectures and technology for scalable, low-latency, and reliable AI-driven cyber-physical systems in NextG networks, with broad impact across industrial automation, robotics, and intelligent infrastructure. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Research team

  • Victor LawrencePI
  • Yu-Dong YaoCo-PI

LOADING MOONBASE SCORE

Directorate for Technology, Innovation, and Partnerships01002627DB NSF RESEARCH & RELATED ACTIVIT01002627RB NSF RESEARCH & RELATED ACTIVIT01002829DB NSF RESEARCH & RELATED ACTIVIT01002728DB NSF RESEARCH & RELATED ACTIVIT01002728RB NSF RESEARCH & RELATED ACTIVITReimbursable/Reserved Out-year, GVF - Global Venture Fund, TIP-CHIPS KTA-6 Communicationsreviewtransmittingscalableinterdisciplinaryproductivitydecisionconnectedestablishamonginclude

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