Collaborative Research: CNS: Medium: Scalable Learning from Distributed Data for Wireless Network Management

合作研究：CNS：媒介：无线网络管理的分布式数据可扩展学习

• 批准号: 2107239
• 负责人: Vladimir Braverman
• 金额: $ 19.99万
• 依托单位: Johns Hopkins University
• 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: 2021-10-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107239&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Medium; Scalable

The transition to 5G is expected to witness not only an emergence of new applications such as mobile augmented and virtual reality, but also opens up the attack surface to both known, and previously unknown threats. Thus, wireless networks of the future will need better control and management at different temporal and traffic aggregation granularities (e.g., how to allocate spectrum, how to quarantine distributed attacks etc.). This project aims to develop scalable, machine learning based analytics on the data from a large set of geographically distributed wireless core network entities such as base stations. The research will enable new approaches for: (a) compressing the raw data via novel summaries and sketches, that reduce overhead while simultaneously enabling highly accurate scalable analytics (b) scalable yet highly flexible distributed learning approaches that are built upon the emerging federated learning paradigm and (c) flexible allocation of bandwidth to support the control plane analytics that minimizes the impact on the data plane. The proposed research outcomes will be systems, algorithms, and data analytics workflows that will inform the design and management of next generation critical wireless infrastructures. The approaches developed will enable ISPs to better apportion resources and enable better performance for emerging augmented reality applications for societal benefit (e.g., disaster response and management). In addition, the approaches can enable the discovery and profiling of new threats, which will have significant implications on national security. The proposed education activities are expected to provide students with a comprehensive training in networking, security, system building, and data science. Thus, there is significant potential for broader impact in terms of contributions to workforce development in an area of national need.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.

预计到5G的过渡不仅会见证新应用的出现，例如移动增强和虚拟现实，而且还可以向已知的攻击表面和以前未知的威胁打开攻击表面。因此，未来的无线网络将需要在不同的时间和流量聚集粒度（例如，如何分配频谱，如何隔离分布式攻击等）上更好地控制和管理。 该项目旨在从一组地理上分布的无线核心网络实体（例如基站）中开发出可扩展的，基于机器学习的分析。该研究将启用以下新方法：（a）通过新颖的摘要和草图压缩原始数据，这些摘要和草图同时启用了高度准确的可扩展分析（b）可扩展但高度灵活的分布式学习方法建立在新兴联合的学习范式上，并且（c）对bandwidth的稳定分配的平面构建了对控制平面的稳定分配。拟议的研究结果将是系统，算法和数据分析工作流，这些工作流将为下一代关键的无线基础架构的设计和管理提供信息。开发的方法将使ISP能够更好地分配资源，并为新兴的增强现实应用程序提供更好的绩效，以获得社会利益（例如，灾难响应和管理）。此外，这些方法可以实现新威胁的发现和分析，这将对国家安全产生重大影响。 拟议的教育活动有望为学生提供有关网络，安全，系统构建和数据科学的全面培训。因此，在国家需求领域对劳动力发展的贡献方面，具有更大的影响。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来获得支持的。