IMR: MM-1C: Learning-driven Models for 5G Internet Measurements

IMR：MM-1C：5G 互联网测量的学习驱动模型

基本信息

批准号：
2220292
负责人：
Georgios Giannakis
金额：
$ 60万
依托单位：
University of Minnesota-Twin Cities
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-10-01 至 2025-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2220292&HistoricalAwards=false
关键词：
IMR MM 1C Learning driven

项目摘要

Enriched with exciting applications providing `smart services,’ next-generation (NextG) cellular networks will fundamentally transform today’s perception of the Internet. Leveraging major advances in artificial intelligence (AI) and machine learning (ML), the envisioned NextG Internet measurement infrastructure will markedly enhance network monitoring, management and deployment. Toward this goal, formidable challenges emerge as measurements can be diverse, complex, and even unavailable. In this context, the present project advocates a learning-driven methodology for systematic, robust, and large-scale NextG Internet measurement infrastructure. This methodology will be informed by extensive, in-depth measurements conducted, and will further utilize the platform and measurement tools developed.Key intellectual advances are pursued in three intertwined research thrusts: T1) novel ensembles of Gaussian Processes to model rich, cross-layer features, and environment, user, and application dynamics; T2) innovative Bayesian active sampling to guide the measurement process; and, T3) path-breaking tools to de-bias, de-noise, and integrate crowd-sourced measurements that may contain missing features (e.g., due to privacy protection); and may be biased, conflicting, or even adversarial. Outcomes will be integrated with an available measurement platform to improve the sampling process, data curation, and analytics that will be continually refined and validated.This research will broadly impact the measurement, design, deployment, and operation of NextG networks, through novel applications and services, many yet to be imagined, thereby bringing significant benefits to the society at large. The project also offers opportunities for engaging undergraduate and K-12 students -- especially women and underrepresented minority groups -- in integrated research, education, and outreach activities. Through collaborations with Industry, the technology transferred will influence the development of NextG networks. In addition, the novel Internet measurement tools will come with open-source software to render the models, codes, datasets, evaluation tests and pertinent artifacts, publicly available. Project’s URL https://spincom.umn.edu/research/currently-funded-projects/nsf2220292This 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.

下一代 (NextG) 蜂窝网络丰富了提供“智能服务”的令人兴奋的应用程序，将从根本上改变当今对互联网的看法，利用人工智能 (AI) 和机器学习 (ML) 方面的重大进步，设想的 NextG 互联网测量基础设施将。显着增强网络监控、管理和部署的目标，由于测量可能多种多样、复杂甚至不可用，因此出现了巨大的挑战。在这种背景下，本项目提倡采用学习驱动的方法来实现系统性、稳健性和大规模性。 NextG 互联网测量基础设施。该方法将通过进行广泛、深入的测量来提供信息，并将进一步利用所开发的平台和测量工具。关键的智力进步是在三个相互交织的研究重点中追求的：T1）新颖的高斯过程集成来建模丰富的跨层功能以及环境、用户和应用动态；T2) 指导测量过程的创新贝叶斯主动采样；以及 T3) 用于消除偏差、消除噪声和集成的开创性工具；可能包含缺失特征（例如，由于隐私保护）的众包测量结果将与可用的测量平台集成，以改进采样过程、数据管理和分析。将不断完善和验证。这项研究将通过新颖的应用和服务（许多尚未想象）广泛影响 NextG 网络的测量、设计、部署和运营，从而为整个社会带来重大利益。提供参与的机会通过与行业合作，转让的技术将影响 NextG 网络的发展。将附带开源软件来呈现模型、代码、数据集、评估测试和相关工件，并公开提供项目的 URL。 https://spincom.umn.edu/research/currently-funded-projects/nsf2220292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。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Multi-Modal Vehicle Data Delivery via Commercial 5G Mobile Networks: An Initial Study

通过商用 5G 移动网络进行多模式车辆数据传输：初步研究

DOI：
10.1109/icdcsw60045.2023.00026
发表时间：
2023
期刊：
2023 IEEE 43rd International Conference on Distributed Computing Systems Workshops (ICDCSW
影响因子：
0
作者：
Carpenter, Jason;Ye, Wei;Qian, Feng;Zhang, Zhi-Li
通讯作者：
Zhang, Zhi-Li

Surrogate Modeling for Bayesian Optimization Beyond a Single Gaussian Process