CCSS: Online Learning for IoT Monitoring and Management
CCSS:物联网监控和管理在线学习
基本信息
- 批准号:2126052
- 负责人:
- 金额:$ 41.5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-09-01 至 2024-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
At the core of several emerging technological advances lies the notion of Internet-of-Things (IoT). Conceptually, IoT is envisioned as an intelligent network infrastructure with a huge number of ubiquitous smart devices present in diverse application domains such as smart buildings, group and personalized healthcare, as well as self-driving connected vehicles, to name a few. Today, a number of IoT applications have already improved many aspects of daily life. However, critical challenges need to be addressed before embracing the full potential of IoT. This in turn calls for innovative machine learning approaches that account for scalability, heterogeneity, adaptivity, and robustness to unpredictable uncertainties -- what are the central challenges facing the emerging IoT monitoring and management tasks. Novel algorithms and their performance need to leverage recent advances in data science, optimization, statistical signal processing, communications, and networking. In addition to markedly influencing future IoT modules, insights gained from this project's learning and inference will also cross-fertilize benefits to a gamut of additional domains, including smart grids, smart cities, and self-driving vehicles. At a broader scale, the developed technologies will provide valuable tools for foundational science and engineering research, and advocate societal embracing of the emergent IoT technologies. Broader impact will be further effected by the integration of research with an educational plan designed to train the new cadre of next-generation of IoT professionals, as well as foster cross-pollination of academic research to industry needs, while promoting and embracing diversity in Science and Engineering.To address the core IoT challenges, this project puts forth foundational tools for real-time interactive function learning using an ensemble of experts. Learning algorithms will be developed with adaptivity and quantifiable performance even in environments with unpredictable dynamics, but also with ability to scale in terms of i) the huge number of ‘Things’ in IoT; ii) the high-dimensional feature vectors involved in sophisticated learning tasks; and iii) the massive data collected, processed, and exchanged over the IoT graph – what is desired for IoT monitoring. Scalability, adaptivity, and robustness benefits in learning nonlinear functions will be further permeated to interactive black-box Bayesian optimization, and reinforcement learning with an ensemble of experts -- merits that will boost performance in open- and closed-loop IoT management.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.
几项新兴技术进步的核心是物联网(IoT)的概念。从概念上讲,物联网被设想为一种智能网络基础设施,其中大量无处不在的智能设备存在于不同的应用领域,例如智能建筑,团体和个性化医疗保健以及自动驾驶联网车辆等。如今,许多物联网应用已经改善了日常生活的许多方面。然而,在充分发挥物联网的潜力之前,需要解决关键的挑战。这反过来又需要创新的机器学习方法,以解决可扩展性、异构性、适应性和对不可预测的不确定性的鲁棒性-新兴物联网监控和管理任务面临的核心挑战是什么。新算法及其性能需要利用数据科学、优化、统计信号处理、通信和网络方面的最新进展。除了显著影响未来的物联网模块外,从该项目的学习和推理中获得的见解还将为包括智能电网、智能城市和自动驾驶汽车在内的其他领域带来交叉效益。在更广泛的范围内,开发的技术将为基础科学和工程研究提供有价值的工具,并倡导社会接受新兴的物联网技术。通过将研究与教育计划相结合,将进一步扩大影响,该计划旨在培养下一代物联网专业人员的新骨干,并促进学术研究与行业需求的交叉,同时促进和拥抱科学和工程的多样性。该项目提出了使用专家集合进行实时交互函数学习的基本工具。即使在具有不可预测动态的环境中,学习算法也将具有自适应性和可量化的性能,而且还具有以下方面的扩展能力:i)物联网中大量的“事物”; ii)复杂学习任务中涉及的高维特征向量;以及iii)通过物联网图收集,处理和交换的大量数据-物联网监控所需的数据。学习非线性函数的可扩展性、自适应性和鲁棒性优势将进一步渗透到交互式黑盒贝叶斯优化中,和强化学习与专家的合奏-优点,这将提高性能,在开放和封闭-该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查进行评估,被认为值得支持的搜索.
项目成果
期刊论文数量(21)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Time-Domain Generalization of Kron Reduction
Kron 约简的时域推广
- DOI:10.1109/lcsys.2022.3185939
- 发表时间:2023
- 期刊:
- 影响因子:3
- 作者:Singh, Manish K.;Dhople, Sairaj;Dorfler, Florian;Giannakis, Georgios B.
- 通讯作者:Giannakis, Georgios B.
Scalable Bayesian Meta-Learning through Generalized Implicit Gradients
- DOI:10.1609/aaai.v37i9.26337
- 发表时间:2023-03
- 期刊:
- 影响因子:0
- 作者:Yilang Zhang;Bingcong Li;Shi-Ji Gao;G. Giannakis
- 通讯作者:Yilang Zhang;Bingcong Li;Shi-Ji Gao;G. Giannakis
Identifying Dependent Annotators in Crowdsourcing
识别众包中的依赖注释器
- DOI:10.1109/ieeeconf56349.2022.10052052
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Traganitis, Panagiotis A.;Giannakis, Georgios B.
- 通讯作者:Giannakis, Georgios B.
Incremental Ensemble Gaussian Processes
- DOI:10.1109/tpami.2022.3157197
- 发表时间:2021-10
- 期刊:
- 影响因子:23.6
- 作者:Qin Lu;G. V. Karanikolas;G. Giannakis
- 通讯作者:Qin Lu;G. V. Karanikolas;G. Giannakis
Learning while Respecting Privacy and Robustness to Adversarial Distributed Datasets
- DOI:10.23919/eusipco55093.2022.9909977
- 发表时间:2022-08
- 期刊:
- 影响因子:0
- 作者:A. Sadeghi;G. Giannakis
- 通讯作者:A. Sadeghi;G. Giannakis
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Georgios Giannakis其他文献
Georgios Giannakis的其他文献
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{{ truncateString('Georgios Giannakis', 18)}}的其他基金
Collaborative Research: ECCS-CCSS Core: Resonant-Beam based Optical-Wireless Communication
合作研究:ECCS-CCSS核心:基于谐振光束的光无线通信
- 批准号:
2332173 - 财政年份:2024
- 资助金额:
$ 41.5万 - 项目类别:
Standard Grant
Collaborative Research: CIF: Medium: Robust Learning over Graphs
协作研究:CIF:媒介:图上的鲁棒学习
- 批准号:
2312547 - 财政年份:2023
- 资助金额:
$ 41.5万 - 项目类别:
Continuing Grant
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
- 批准号:
2220292 - 财政年份:2022
- 资助金额:
$ 41.5万 - 项目类别:
Standard Grant
Collaborative Research: SWIFT: Cognitive-IoV with Simultaneous Sensing and Communications via Dynamic RF Front End
合作研究:SWIFT:通过动态射频前端实现同步传感和通信的认知车联网
- 批准号:
2128593 - 财政年份:2021
- 资助金额:
$ 41.5万 - 项目类别:
Standard Grant
Hybrid mmWave mMIMO Transceiver Design for Doubly-Selective Channels
适用于双选通道的混合毫米波 mMIMO 收发器设计
- 批准号:
2102312 - 财政年份:2020
- 资助金额:
$ 41.5万 - 项目类别:
Standard Grant
CPS: Medium: Collaborative Research: Collective Intelligence for Proactive Autonomous Driving (CI-PAD)
CPS:中:协作研究:主动自动驾驶集体智慧 (CI-PAD)
- 批准号:
2103256 - 财政年份:2020
- 资助金额:
$ 41.5万 - 项目类别:
Standard Grant
CIF: Medium: Adaptive Diffusions for Scalable and Robust Learning over Graphs
CIF:中:用于图上可扩展和鲁棒学习的自适应扩散
- 批准号:
1901134 - 财政年份:2019
- 资助金额:
$ 41.5万 - 项目类别:
Standard Grant
CCSS: Collaborative Research: Learn-and-Adapt to Manage Dynamic Cyber-Physical Networks
CCSS:协作研究:学习和适应管理动态信息物理网络
- 批准号:
1711471 - 财政年份:2017
- 资助金额:
$ 41.5万 - 项目类别:
Standard Grant
CCSS: Collaborative Research: Smart-Grid Powered Green Communications in Heterogeneous Networks
CCSS:协作研究:异构网络中智能电网驱动的绿色通信
- 批准号:
1508993 - 财政年份:2015
- 资助金额:
$ 41.5万 - 项目类别:
Standard Grant
EAGER-DynamicData: Judicious Censoring, Random Sketching, and Efficient Validate for Learning Patterns from Dynamically-Changing and Large-Scale Data Sets
EAGER-DynamicData:明智的审查、随机草图和高效验证,用于从动态变化的大规模数据集中学习模式
- 批准号:
1500713 - 财政年份:2015
- 资助金额:
$ 41.5万 - 项目类别:
Standard Grant
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