CAREER: Towards Networked Airborne Computing in Uncertain Airspace: A Control and Networking Facilitated Distributed Computing Framework

职业：走向不确定空域的网络机载计算：控制和网络促进的分布式计算框架

• 批准号: 2048266
• 负责人: Junfei Xie
• 金额: $ 55.67万
• 依托单位: San Diego State University Foundation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-15 至 2026-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2048266&HistoricalAwards=false
• 关键词: CAREER; Towards; Networked; Airborne; Computing

Networked airborne computing is a new computing paradigm that relies on the airborne network formed by aerial vehicles with direct flight-to-flight communication links to achieve computing in the air. It can not only benefit a wide range of existing applications (e.g., drone show, precision agriculture and emergence response), but also give rise to a variety of new applications (e.g., next-generation air traffic control, Internet of Things and real-time 3-dimensional mapping). Despite the exciting applications, enabling networked airborne computing requires overcoming many formidable challenges such as 3-dimensional node mobility, highly uncertain operating environment and strict safety requirements. This project aims to develop an innovative theoretical framework that conquers these challenges to enable networked airborne computing. Unlike traditional mobile computing systems that treat mobility, uncertainty and communication as constraints for computing, the proposed framework exploits their benefits to facilitate robust, efficient and scalable computing in the uncertain, dynamic and heterogeneous airspace. The proposed framework, featured by fast response, resilience to uncertain disturbances and failures, scalability, and adaptivity to system and environmental changes, will advance the fundamental Computer and Network Systems (CNS) science and engineering on mobile computing and produce broader civilian and economic impacts related to unmanned systems based applications. The findings of this project will also contribute to fields such as distributed computing, stochastic optimal control, networking, real-time decision-making under uncertainty and experiment design. Innovative education and outreach activities (e.g., virtual lab modules, summer camps and internships) will produce a profound impact on the community and significantly benefit students, especially underrepresented minorities, at different levels in San Diego county and beyond. The proposed dissemination activities (e.g., workshops and tutorials) will further broaden the impact of the proposed research and benefit the general public.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.

网络化机载计算是一种新的计算范式，依靠具有直接航班间通信链路的飞行器形成的机载网络来实现空中计算。它不仅可以惠及广泛的现有应用（例如无人机表演、精准农业和紧急响应），而且还可以催生各种新应用（例如下一代空中交通管制、物联网和现实世界）。时间 3 维映射）。尽管应用令人兴奋，但实现网络机载计算需要克服许多艰巨的挑战，例如 3 维节点移动性、高度不确定的操作环境和严格的安全要求。该项目旨在开发一个创新的理论框架来克服这些挑战，以实现网络机载计算。与将移动性、不确定性和通信视为计算约束的传统移动计算系统不同，所提出的框架利用它们的优势来促进不确定、动态和异构空域中稳健、高效和可扩展的计算。所提出的框架具有快速响应、对不确定干扰和故障的弹性、可扩展性以及对系统和环境变化的适应性等特点，将推动移动计算方面的基础计算机和网络系统（CNS）科学与工程，并产生更广泛的民用和经济影响与基于无人系统的应用相关。该项目的研究成果还将对分布式计算、随机最优控制、网络、不确定性下的实时决策和实验设计等领域做出贡献。创新教育和推广活动（例如虚拟实验室模块、夏令营和实习）将对社区产生深远影响，并使圣地亚哥县及其他地区不同级别的学生，特别是代表性不足的少数族裔受益匪浅。拟议的传播活动（例如研讨会和教程）将进一步扩大拟议研究的影响并使公众受益。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进行评估，被认为值得支持标准。

项目成果

Coded Distributed Path Planning for Unmanned Aerial Vehicles

无人机编码分布式路径规划

• DOI: 10.2514/6.2021-2378
• 发表时间: 2021-07
• 期刊: AIAA AVIATION 2021 FORUM
• 作者: Douma, Cameron;Wang, Baoqian;Xie, Junfei
• 通讯作者: Xie, Junfei

Risk-bounded and fairness-aware path planning for urban air mobility operations under uncertainty

不确定性下城市空中交通运行的风险限制和公平意识路径规划

• DOI: 10.1016/j.ast.2022.107738
• 发表时间: 2022-08
• 期刊: Aerospace Science and Technology
• 影响因子: 5.6
• 作者: Wu, Pengcheng;Xie, Junfei;Liu, Yanchao;Chen, Jun
• 通讯作者: Chen, Jun

Exploring Networked Airborne Computing: A Comprehensive Approach with Advanced Simulator and Hardware Testbed

探索网络机载计算：采用高级模拟器和硬件测试台的综合方法

• DOI: 10.1142/s2301385024420056
• 发表时间: 2023-11
• 期刊: Unmanned Systems
• 影响因子: 5.3
• 作者: Zhang, Haomeng;Wang, Baoqian;Wu, Ruitao;Xie, Junfei;Wan, Yan;Fu, Shengli;Lu, Kejie
• 通讯作者: Lu, Kejie

Evaluating the Impact of Onboard and Offboard Computing on UAS Traffic Management

评估机载和机外计算对无人机交通管理的影响

• DOI: 10.2514/6.2023-3858
• 发表时间: 2023-06
• 期刊: AIAA AVIATION 2023 Forum
• 作者: Essick, Drake;Bautista, Luiz Gonzalez;Xie, Junfei;Wan, Yan;Chen, Jun
• 通讯作者: Chen, Jun

CFL-HC: A Coded Federated Learning Framework for Heterogeneous Computing Scenarios

CFL-HC：异构计算场景的编码联邦学习框架

• DOI: 10.1109/globecom46510.2021.9685962
• 发表时间: 2021-12
• 期刊: 2021 IEEE Global Communications Conference (GLOBECOM
• 作者: Wang, Dong;Wang, Baoqian;Zhang, Jinran;Lu, Kejie;Xie, Junfei;Wan, Yan;Fu, Shengli
• 通讯作者: Fu, Shengli