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：
Georgios Giannakis
金额：
$ 41.5万
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：
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：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126052&HistoricalAwards=false
关键词：
CCSS Online Learning IoT Monitoring

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.

物联网 (IoT) 概念是多项新兴技术进步的核心，从概念上讲，物联网被设想为一种智能网络基础设施，在智能建筑、智能群组等不同应用领域中存在大量无处不在的智能设备。如今，许多物联网应用已经改善了日常生活的许多方面，但在充分发挥物联网的潜力之前，还需要解决一些关键挑战。这反过来又需要创新的机器学习考虑可扩展性、异构性、适应性和对不可预测的不确定性的鲁棒性的方法——新兴物联网监控和管理任务面临的主要挑战是什么，以及它们的性能需要利用数据科学、优化、统计算法、通信方面的最新进展。除了显着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一系列其他领域带来交叉效益，包括智能电网、智能城市和自动驾驶汽车。在更广泛的范围内，所开发的技术将为基础科学和工程研究提供宝贵的工具，并倡导普遍采用新兴的物联网技术，将研究与旨在培训下一代新干部的教育计划相结合，将进一步产生更广泛的影响。 -培养物联网专业人员，并促进学术研究与行业需求的交叉授粉，同时促进和拥抱科学和工程的多样性。为了解决核心物联网挑战，该项目提出了实时交互式功能学习的基础工具使用一组专家的学习算法将。即使在动态不可预测的环境中，也能具有适应性和可量化的性能，而且能够在 i) 物联网中的大量“事物”方面进行扩展；ii) 复杂学习任务中涉及的高维特征向量； iii) 通过物联网图收集、处理和交换的海量数据——学习非线性函数的可扩展性、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和强化学习中。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。