Collaborative Research: CISE MSI: RDP: NeTS: Enabling Spectrum‑Efficient Wireless Coexistence for Heterogeneous IoT over 2.4 GHz: A Hardware–Software Framework Co‑Design

Synopsis

This project investigates spectrum-efficient coexistence among heterogeneous IoT protocols operating in the crowded 2.4 GHz unlicensed band. By developing a hardware–software co-design communication framework and leveraging SDR-based AI coordination, the research enables parallel communication across Wi-Fi, ZigBee, and BLE devices, fundamentally improving spectrum utilization and power efficiency for next-generation IoT systems.

Personnel

• Lead PI: Dr. Bayanar Arigong from Florida A&M University (FAMU)
• PI: Dr. Linke Guo from Clemson University Bayanar Arigong (CU)
• PI: Dr. Xiaonan Zhang from Florida State University (FSU)
• Graduate Research Assistant: Shaoying Wang (FSU)

Collaborators

• Florida Agricultural & Mechanical University (FAMU)
• Clemson University (CU)
• Florida State University (FSU)

Publications

• Li, G.; Zhang, X.; Lin, C.; Guo, L. (2024). RF Sensing‑enabled Interference Mitigation for Wi‑Fi‑based IoT Systems: A Deep Learning Approach.
• Lin, C.; Guo, L.; Jiang, C.; Zhang, X. (2025). Achieving Robust Resource Orchestration for Highly Dense Heterogeneous IoT Systems. IEEE INFOCOM 2025.
• Guo, L.; Zhang, X.; Li, G.; Lin, C.; Ma, X. (2025). Adversarial‑Robust ViT‑Based Automatic Modulation Recognition in Practical DL‑based Wireless Systems. IEEE S&P 2025.
• Wang, S.; Yuan, Y.; Zhou, H.; Zhang, X. (2025). Non‑Intrusive Speaker Diarization via mmWave Sensing. Poster, ACM SenSys 2025.

Code & Data

• MARL-based Resource orchestration in dense indoor IoT deployments — GitHub (coming soon)
• GAN-based CSI‑Driven RF Mapping — Dataset(coming soon)
• Testbed specifications & reproducibility notes — PDF (coming soon)

Educational Activities

• Integrated project results into CIS 5930 (Special Topics in IoT) and CNS 4504/5505 (Computer Networks), covering wireless coexistence, spectrum sharing, and mmWave sensing.
• Mentored students on reinforcement learning, spectrum management，and IoT systems (mmWave); supported paper publications via ACM SenSys poster and papers at IEEE CNS, INFOCOM, and S&P.
• Cross‑institution mentoring through dissertation committee service at FAMU.

Broader Impacts

• Provides new cross-layer insights that integrate sensing, learning, and networking, pushing the discipline toward intelligent and scalable IoT systems.
• Strengthens collaboration among FSU, FAMU (MSI), and Clemson; engages students in wireless communications and networking.
• Advances spectrum‑ and power‑efficient coexistence with potential applications in smart healthcare and industrial IoT.