PROJECT
- NeTS: Small: AutoSlicing: Safe Online Autonomous Network Orchestration Towards Pervasive Slicing-as-a-Service [Link]
- Funding Source: NSF CNS 2333164
- Total Budget: $531,134
- Role: Sole Principal Investigator
- Duration: Sept. 2024 – Aug. 2027
- Abstract: This project outlines a first-in-its-kind autonomous network orchestration framework towards pervasive slicing-as-a-service. It addresses the continual domain shifting issue by automatically bridging simulation-to-reality gap via offline augmenting simulators and safely adapting time-varying dynamics via online learning in real-world networks. It will be evaluated in UNL site-scale and PAWR city-scale platforms.
- Roadside-to-Vehicle Crash Avoidance Warning System for Commercial Motor Vehicles on Rural Roads [Link]
- Funding Source:
- Total Budget: $1,342,761
- Role: Co-Principal Investigator (with PI Nathan Huynh, and other Co-PIs Li Zhao and Mehmet Vuran, Mizan Rahman@UA, and Eren Erman Ozguven@FAMU-FSU)
- Duration: Sept. 2024 – Sept. 2026
- Abstract: The goal of this project is to advance the safety protocols for commercial motor vehicles (CMVs) by integrating cutting-edge road hazard condition detection technologies in real-time. By using this new approach, we aim to minimize the likelihood of crashes in strategic locations on rural roadways.
- CropTwin: Automatic Digital Twin for Crop Growth Modeling towards Smart Irrigation Management [Link]
- Funding Source: USDA NIFA AFRI
- Total Budget: $300,000
- Role: Principal Investigator (with Co-PI Hongzhi Guo, Yufeng Ge, and Saleh Taghvaeian)
- Duration: Sept. 2024 – Aug. 2026
- Abstract: The goal is to design, develop, and deploy new digital twin systems in regard to crop growth modeling for smart irrigation management. We propose the CropTwin system with three core research tasks, including cost-efficient IoT systems, automatic digital twining, and smart irrigation solutions. We deploy and evaluate CropTwin in the real-world research farm at UNL, throughout a full soybean growth session.
- Bring Advanced Cellular Networks From Earth to Space [Link]
- Funding Source: NASA Nebraska Space Mini Grant
- Total Budget: $5,000
- Role: Sole Principal Investigator
- Duration: Sept. 2024 – Apr. 2025
- Abstract: Envisioning the on-the-horizon site-scale space missions in the next decades (e.g., mining and agriculture bases), advanced wireless technologies are indispensable to effectively connect different kinds of devices, such as robots and facilities. Existing space wireless solutions are manually customized (e.g., hardware and protocol) for specified space missions with limited interoperability and small-scale low-performance networking capabilities. In this project, we aim to bring state-of-the-art cellular network technologies on Earth (e.g., 5G and Beyond) to the space.
- CC* Integration-Large: Husker-Net: Open Nebraska End-to-End Wireless Edge Networks [Link]
- Funding Source: NSF OAC 2321699
- Total Budget: $891,000
- Role: Principal Investigator (with Co-PI Mehmet Vuran and Toolika Ghose)
- Duration: Oct. 2023 – Sept. 2025
- Abstract: This project outlines a novel open end-to-end cellular edge network (Husker-Net) by designing, deploying, and operating private 5G network over a light-licensed CBRS spectrum in multiple UNL campuses. Husker-Net is featured with ultra-low operating cost with open-source modules, flexible deployment with both wired and wireless backhaul (e.g., LEO in mid of Nebraska), and zero-touch management with automatic model-free algorithms.
- CNS Core: Medium: Field-Nets: Field-to-Edge Connectivity for Joint Communication and Sensing in Next-Generation Intelligent Agricultural Networks [Link]
- Funding Source: NSF CNS 2212050
- Total Budget: $1,000,000
- Role: Co-Principal Investigator (with PI Mehmet Vuran, and Co-PI Shuai Nie and Christos Argyropoulos)
- Duration: Oct. 2022 – Sept. 2025
- Abstract: In this project, an interdisciplinary team of experts in millimeter-wave communications, metamaterial and metasurface-inspired antenna array design, dynamic spectrum access, and radio access networks in collaboration with experts in agricultural robotics and sensor-based plant phenotyping aim to provide connectivity to rural farm fields and increase national competence to bring new technologies to rural America rapidly.
- Real-World Machine Learning in Mobile Network Slicing [COMPLETED]
- Funding Source: Nebraska EPSCoR FIRST Award
- Total Budget: $25,000
- Role: Sole Principal Investigator
- Duration: Dec. 2022 – Dec. 2023
- Abstract: This project outlines a novel framework that resolves the simulation-to-reality discrepancy for intelligent and autonomous mobile network slicing with new-designed real-world machine learning techniques. We will investigate the research thrust of simulator augmentation with real-to-sim learning, policy adaptation with sim-to-real transfer, and online network management with safe policy learning.