CAREER: Toward A Machine Learning Framework for the Internet of Things

职业：构建物联网机器学习框架

• 批准号: 1553340
• 负责人: Stacy Patterson
• 金额: $ 61.87万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1553340&HistoricalAwards=false
• 关键词: CAREER; Toward; Machine; Learning; Framework

This project will develop a new paradigm and tools for machine learning that can cope with the massive-scale, geographically distributed data in the Internet of Things. The key innovation is to use devices and computing power within the Internet of Things network itself to perform data analysis in a scalable, reliable fashion.The Internet of Things describes a network of devices, from RFID tags, to smart thermostats, to light bulbs, that can sense and communicate information. It is predicted that by 2020, there will be 25 to 50 billion devices in the Internet of Things. This massive network and the data it generates will enable new applications in a wide range of critical domains including environmental management, smart infrastructure, and healthcare. To achieve this vision, it is crucial to be able to quickly analyze and learn from the massive amount of generated data. Current approaches for big data analytics require full data transfer to a platform with large computational power, such as the cloud. Given the projected explosion in the number of devices and the resulting data generation rate, this is not feasible. The proposed research integrates tools and theory from machine learning, distributed computing, and networked systems in three main thrusts that include; a computational framework that provides an abstraction for algorithm design and implementation that is flexible enough to support a wide collection of machine learning methods, a framework implementation that provides a stable platform for algorithm developers by masking device heterogeneity, devices failures, and the network dynamics of the Internet of Things, and development and implementation of techniques to adapt the network and computation to support algorithm execution with performance guarantees.The framework developed in this project will facilitate rapid development and deployment of Internet of Things applications. A significant contribution will be an open source software implementation of the framework allowing others with limited network expertise to develop their own applications for the Internet of Things. The project also includes robust educational and outreach components including graduate and undergraduate research, curriculum development, and activities to promote and support the participation of women in computer science.

该项目将开发一种新的机器学习范式和工具，能够应对物联网中大规模、地理分布的数据。关键的创新是利用物联网网络本身的设备和计算能力以可扩展、可靠的方式执行数据分析。物联网描述了一个可以感知和传达信息的设备网络，从 RFID 标签到智能恒温器，再到灯泡。预计到2020年，物联网设备数量将达到25至500亿台。这个庞大的网络及其生成的数据将为环境管理、智能基础设施和医疗保健等广泛关键领域的新应用提供支持。为了实现这一愿景，能够快速分析大量生成的数据并从中学习至关重要。 当前的大数据分析方法需要将数据全部传输到具有强大计算能力的平台，例如云。考虑到预计设备数量的爆炸性增长以及由此产生的数据生成率，这是不可行的。 拟议的研究整合了机器学习、分布式计算和网络系统的工具和理论，主要包括以下三个方面：一个计算框架，为算法设计和实现提供抽象，足够灵活，支持广泛的机器学习方法；一个框架实现，通过屏蔽设备异构性、设备故障和物联网的网络动态，为算法开发人员提供稳定的平台；开发和实现适应网络和计算以支持算法执行并保证性能的技术。该框架开发 该项目将促进物联网应用的快速开发和部署。一个重大贡献将是该框架的开源软件实现，允许网络专业知识有限的其他人开发自己的物联网应用程序。该项目还包括强大的教育和外展部分，包括研究生和本科生研究、课程开发以及促进和支持女性参与计算机科学的活动。