Collaborative Research:CISE-MSI:DP:CNS:Enabling On-Demand and Flexible Mobile Edge Computing with Integrated Aerial-Ground Vehicles

The rapid proliferation of mobile and Internet-of-Things devices has revolutionized various aspects of our lives. However, the enormous amount of data generated by these devices poses significant challenges for wireless-communication infrastructure, which has limited radio spectrum. Additionally, many emerging applications require low-latency and computation-intensive processing, making the traditional cloud-centric approach inadequate. To address these challenges, this project proposes an innovative solution called Aerial-Ground Intelligent vehicular Edge (AGILE) which leverages the capabilities of aerial and ground vehicles with artificial-intelligence-processing capabilities to create an on-demand, flexible, and cost-effective mobile-edge-computing (MEC) system. AGILE aims to provide ubiquitous and low-latency computing services to support massive connected devices and enable efficient data processing. The project focuses on designing the AGILE architecture, which integrates aerial and ground vehicles into a 3D network for intelligent MEC service provisioning. Firstly, the research investigates collaborative training schemes between unmanned aerial vehicles (UAVs) and ground vehicles to enable fast and energy-efficient federated learning for intelligent MEC services. Secondly, the project addresses the coupling issue of UAV positioning, communication, and computing-resource allocation, optimizing them for on-demand MEC service provisioning. Finally, dynamic UAV movement and resource-reconfiguration schemes are developed to adaptively meet user demand and to achieve flexible MEC service provisioning in the presence of varying ground-vehicle resources. This project will strengthen the existing research collaborations among the three participating minority-serving institutions, while fostering research involvement of African American/Black, Hispanic, and women undergraduate and/or graduate students with the knowledge and skills to contribute to the fields of MEC and artificial intelligence. Those underserved students will benefit from this project through research projects, classroom teaching, and senior-design projects. Such participation will help all institutes in improving underrepresented students' retention rates. 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.