Collaborative Research: CPS Medium: Learning through the Air: Cross-Layer UAV Orchestration for Online Federated Optimization

合作研究：CPS 媒介：空中学习：用于在线联合优化的跨层无人机编排

• 批准号: 2313110
• 负责人: Cong Shen
• 金额: $ 40万
• 依托单位: University of Virginia Main Campus
• 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=2313110&HistoricalAwards=false
• 关键词: Collaborative; Research; CPS; Medium; Learning

Propelled by the growth in demand for artificial intelligence-enabled applications, the past decade has witnessed the emergence of Collaborative Cyber-Physical Learning Systems (CCPLS). CCPLS carry out distributed, learning-based processing tasks through coordination among Cyber-Physical System (CPS) devices, and are envisioned to provide critical functionality across the commercial and defense sectors in the next several years. However, the data generated by CCPLS is often large-scale, high-dimensional, heterogeneous, and time-varying, which poses critical challenges for intelligence modeling. Concurrently, unmanned vehicles, in particular Unmanned Aerial Vehicles (UAVs), have shown promise of scaling up information-sharing in CCPLS, especially in under-served regions such as rural areas. The project's novelties are in establishing a concrete foundation for UAV-CCPLS integration that unifies the associated learning, networking, and communication design aspects around appropriate intelligence metrics. The project's impacts are the development of UAV-assisted CCPLS for smart agriculture tasks, as well as advancing the manufacturing of UAVs and other unmanned vehicles tailored for CCPLS. Project outcomes will be disseminated by releasing open-source software and research videos and tutorials. The investigators will further engage in Curriculum development, diversity, and outreach activities including mentoring undergraduate researchers. Research investigations center around three interconnected thrusts. Thrust 1 develops a novel UAV-assisted intelligence framework for CCPLS and introduces a precise, task-oriented notion of data dynamics and heterogeneity. Additionally, this thrust develops a `learning for learning’ framework that aims to predictively estimate the rate of data dynamics. Thrust 2 investigates methodologies for jointly optimizing resource utilization and intelligence quality through co-design of UAV trajectories and UAV-to-CPS network establishment. The data dynamics framework from Thrust 1 is integrated into this design through an online, network-aware sequential decision-making framework. Finally, Thrust 3 develops CCPLS communication protocols based on learning-aware uplink and downlink wireless beamformers and over-the-air aggregation methods. These protocols are tailored to the specific needs of the UAV-assisted learning systems, e.g., the transmission of noisy information over UAV-to-UAV and UAV-to-access point communication links.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.

在人工智能应用需求增长的推动下，过去十年见证了协作网络物理学习系统（CCPLS）设备的出现，并且其功能预计将在未来的商业和国防领域提供关键功能。然而，CCPLS生成的数据往往是大规模、高维、异构、时变的，同时，无人驾驶车辆，特别是无人驾驶飞行器（UAV），已经显示出扩大 CCPLS 信息共享的前景，特别是在农村地区等服务不足的地区。为无人机-CCPLS 集成奠定坚实的基础，围绕适当的情报指标统一相关的学习、网络和通信设计方面。该项目的影响是开发用于智能农业任务的无人机辅助 CCPLS，以及推进为 CCPLS 定制的无人机和其他无人驾驶飞行器的制造 项目成果将通过发布开源软件以及研究视频和教程来传播。研究人员将进一步参与课程开发、多样性和外展活动，包括指导本科生。研究中心围绕三个相互关联的主旨进行研究，为 CCPLS 开发了一种新颖的无人机辅助智能框架，并引入了精确的、面向任务的数据动态和异构性概念。 Thrust 2 开发了一个“学习学习”框架，旨在预测性估计数据动态速率，研究通过联合设计无人机轨迹和建立无人机到 CPS 网络框架来联合优化资源利用率和情报质量的方法。图 1 通过在线、网络感知的顺序决策框架集成到此设计中 最后，Thrust 3 开发了基于学习感知上行链路和下行链路的 CCPLS 通信协议。这些协议是根据无人机辅助学习系统的特定需求而定制的，例如通过无人机到无人机和无人机到接入点通信链路传输噪声信息。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。