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

移动的和物联网设备的迅速普及已经彻底改变了我们生活的各个方面。然而，这些设备产生的大量数据对无线通信基础设施构成了重大挑战，因为无线电频谱有限。此外，许多新兴应用程序需要低延迟和计算密集型处理，这使得传统的以云为中心的方法无法胜任。为了应对这些挑战，该项目提出了一种名为空中-地面智能车辆边缘（AGILE）的创新解决方案，该解决方案利用具有人工智能处理能力的空中和地面车辆的能力来创建按需，灵活且具有成本效益的移动边缘计算（MEC）系统。AGILE旨在提供无处不在的低延迟计算服务，以支持大规模连接的设备并实现高效的数据处理。该项目重点设计AGILE架构，将空中和地面车辆集成到3D网络中，以实现智能MEC服务提供。首先，该研究调查了无人机（UAV）和地面车辆之间的协同训练方案，以实现智能MEC服务的快速和节能的联合学习。其次，该项目解决了无人机定位，通信和计算资源分配的耦合问题，优化它们以提供按需MEC服务。最后，开发了动态无人机移动和资源重新配置方案，以自适应地满足用户需求，并在地面车辆资源变化的情况下实现灵活的MEC服务提供。该项目将加强三个参与少数民族服务机构之间的现有研究合作，同时促进非裔美国人/黑人，西班牙裔和女性本科生和/或研究生的研究参与，这些学生具有为MEC和人工智能领域做出贡献的知识和技能。这些服务不足的学生将通过研究项目，课堂教学和高级设计项目从这个项目中受益。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.