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

合作研究：CISE-MSI:DP:CNS：通过集成空地车辆实现按需且灵活的移动边缘计算

• 批准号: 2318663
• 负责人: Yuanxiong Guo
• 金额: $ 20万
• 依托单位: University of Texas at San Antonio
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318663&HistoricalAwards=false
• 关键词: Collaborative; Research; CISE; MSI; DP

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.

移动和贸易互联网设备的快速扩散彻底改变了我们生活的各个方面。但是，这些设备产生的大量数据对无线电频谱有限的无线通信基础设施构成了重大挑战。此外，许多新兴应用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不足。为了应对这些挑战，该项目提出了一种创新的解决方案，称为空中智能车辆边缘（敏捷），该解决方案利用人工智能处理能力来利用航空和地面车辆的能力，以创建一种按需，灵活和成本效益的移动移动和计算机计算（MEC）系统。敏捷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有效的数据处理。该项目着重于设计敏捷体系结构，该建筑将空中和地面车辆整合到3D网络中，用于智能MEC服务提供。首先，该研究调查了无人机（UAV）和地面车辆之间的合作培训计划，以使智能MEC服务的快速和节能联合学习能够。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，并将其优化为按需MEC服务提供。最后，开发了动态无人机运动和资源重新配置方案，以适应用户需求，并在存在不同的地面车辆资源的情况下实现灵活的MEC服务提供。该项目将加强三个参与的少数派服务机构之间的现有研究合作，同时促进对非裔美国人/黑人，西班牙裔，西班牙裔和/或具有知识和技能的妇女的研究参与，以促进MEC和人工智能的领域。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该项目中受益。这种参与将有助于所有机构改善代表性不足的学生的保留率。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。