CAREER: Towards Semantic-Centric Wireless Foundations for Swarm AI

Artificial intelligence (AI) is rapidly expanding from centralized computing infrastructures into the physical world, where networks of distributed devices must sense, reason, and act together in dynamic and uncertain environments. This transformation gives rise to swarm AI, an emerging form of intelligent infrastructure that supports applications such as disaster response, environmental monitoring, precision agriculture, and autonomous mobility. Unlike traditional systems that rely on stable wired connections, swarm intelligence operates over wireless links that are intermittent, noisy, and resource constrained. Communication, therefore, becomes a central bottleneck that limits reliability, efficiency, and coordination. This project establishes a new wireless foundation for swarm AI by prioritizing the meaning and task relevance of transmitted information rather than raw bit accuracy alone. By strengthening how distributed agents share mission-critical information under challenging wireless conditions, the research enhances the resilience, scalability, and interoperability of next-generation intelligent systems. The project integrates research and education through curriculum development in communication-aware AI, hands-on mentoring of undergraduate and graduate students, outreach to K-12 learners, and open dissemination of research outcomes. These activities broaden participation in advanced wireless and intelligent systems research and contribute to workforce development in emerging communication and intelligent system technologies. The project addresses a fundamental gap between AI systems that assume ideal connectivity and wireless communication protocols that optimize bit-level fidelity without accounting for task intent. The scientific problem is how to design wireless architectures that are aware of semantic content, resilient to time-varying channel impairments, and adaptive to heterogeneous device capabilities in swarm settings. The research establishes a semantic-centric communication framework organized into three integrated thrusts: (i) robust semantic transceiver principles that identify and protect task-relevant information against dynamic wireless distortion, ensuring reliable semantic delivery under feature-dependent channel impairments; (ii) swarm-aware radio orchestration strategies that align spectrum allocation and scheduling with collective task objectives through utility-driven coordination; and (iii) heterogeneity-aware collaborative reasoning architectures that enable progressive semantic compression and resource-adaptive inference across devices with diverse sensing, computing, and communication constraints. The research combines theoretical analysis, algorithm design, and experimental validation to advance communication-aware intelligence across layers of the wireless stack. Together, these advances provide a principled foundation for building resilient, scalable, and interoperable swarm AI infrastructures. 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. NSF Award ID: 2542152 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Lan Zhang | Institution: Clemson University, CLEMSON, SC | Award Amount: $551,585 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2542152 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2542152.html