Career: SIM-integrated air-to-ground autonomous vehicle networks

The National Science Foundation Division of Information and Intelligent Systems awarded Texas A&M University-Corpus Christi $365,996 on October 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop and evaluate an air-to-ground autonomous vehicle network integrating stacked intelligent metasurface (SIM) antennas with unmanned aerial vehicles as mobile communication and computing platforms.

The research addresses latency, reliability, and energy consumption challenges in vehicular networks by leveraging UAVs to provide edge computing and communication services, with signal-processing tasks shifted from power-hungry digital hardware to programmable analog metasurface antennas. The system employs AI-driven resource allocation, UAV trajectory planning, and vehicle offloading strategies organized across three research thrusts: predictive multi-agent spatiotemporal intelligence to forecast mobility and quality of service; scalable AI-driven control methods to jointly optimize UAV positioning, vehicle task offloading, radio resource management, and SIM configuration; and integration and experimental validation.

The integrated education plan encompasses undergraduate research engagement, new graduate courses, drone coding and traffic safety challenges, and development of a disaster-relief pop-up connectivity kit for emergency communication. The project will release datasets and open-source software.

Performance occurs at Texas A&M University-Corpus Christi in Corpus Christi, Texas, with a period of performance from October 1, 2026, through September 30, 2031.

Abstract

Existing vehicular networks frequently fall short of the ability to process and transmit large volumes of data with ultra-low latency, high reliability, and low energy consumption. This project develops and evaluates a novel air-to-ground vehicular network to overcome energy and latency bottlenecks at the mobile edge, leveraging unmanned aerial vehicles (uavs) as mobile communication and computing platforms to provide on-demand services. Additionally, selected signal-processing tasks are moved from power-hungry digital hardware to analog domain by exploring programmable stacked intelligent metasurface (SIM) antennas to further improve energy efficiency and resilience. The system is optimized by AI-driven resource allocation, UAV trajectory planning, and vehicle offloading strategies. IT enhances intelligent transportation systems, supports safer transportation, and improves communication during emergencies. The project contains an integrated education plan including undergraduate research engagement, new graduate courses, drone coding and traffic safety challenges. IT also develops a disaster-relief POP-up connectivity kit for emergency communication scenarios, and releases datasets and open-source software. This project aims to develop a comprehensive framework for SIM-integrated air-to-ground autonomous vehicle networks and investigates how programmable wave-domain beamforming, UAV-assisted vehicular edge computing, and AI-driven resource allocation can be jointly designed for uplink task offloading and downlink service delivery in highly mobile wireless environments. The research agenda is organized into three thrusts: (1) developing a predictive, multi-agent spatiotemporal intelligence scheme to predict mobility and SIM-aware quality of service; (2) designing scalable AI-driven control methods to jointly optimize UAV positioning, vehicle task offloading, radio resource management, and SIM configurations for energy-efficient uplink transmissions; and (3) developing hierarchical schemes using advanced reinforcement learning to adapt wireless resources for downlink optimization under dynamic traffic and wireless conditions. Validation is performed with simulation, hardware-in-the-loop testing, and staged experiments. 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.- subawards are not planned for this award.