Collaborative Research: VINES: Track 2: NSF-BF: Mission Critical Semantic Communications in NextG Networks for Smart Manufacturing
- Institution
- Stevens Institute of Technology, NJ
- Principal investigator
- Victor Lawrence
- Funding
- $900K
- Duration
- 2026–2029
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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 Yao
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