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

该项目将为机器学习开发一种新的范例和工具，以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。物联网描述了一个设备网络，从RFID标签到智能恒温器，再到灯泡，这些设备都可以感知和交流信息。据预测，到2020年，物联网设备数量将达到250亿至500亿。这个庞大的网络及其生成的数据将在包括环境管理、智能基础设施和医疗保健在内的广泛关键领域实现新的应用。为了实现这一愿景，能够快速分析并从大量生成的数据中学习至关重要。目前的大数据分析方法需要将全部数据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式增长和由此产生的数据生成速率，这是不可行的。拟议的研究将机器学习，分布式计算和网络系统的工具和理论集成在三个主要重点中，包括；一个为算法设计和实现提供抽象的计算框架，该框架足够灵活，可以支持广泛的机器学习方法；一个通过屏蔽设备异质性、设备故障和物联网网络动态，为算法开发人员提供稳定平台的框架实现；以及开发和实现适应网络和计算的技术，以支持具有性能保证的算法执行。本项目开发的框架将促进物联网应用的快速开发和部署。一个重要的贡献将是框架的开源软件实现，允许其他网络专业知识有限的人开发他们自己的物联网应用程序。该项目还包括强有力的教育和外联部分，包括研究生和本科生研究、课程开发以及促进和支持妇女参与计算机科学的活动。

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.