物联网数据分析的机器学习：综述
Machine learning for Internet of Things data analysis: A survey
摘要：
硬件、软件和通信技术的快速发展使得互联网连接的传感设备得以出现，这些设备可以从物理世界进行观察和数据测量。据估计，到2020年，正在使用的互联网连接设备总数将在250亿至500亿之间。随着数字的增长和技术的成熟，发布的数据量将增加。被称为物联网（IoT）的互联网连接设备技术通过提供物理和网络世界之间的连接和交互来继续扩展当前的互联网。除了数量增加外，物联网还生成大数据，其特征在于时间和位置依赖性方面的速度，具有多种多样的模式和不同的数据质量。对这些大数据的智能处理和分析是开发智能物联网应用的关键。本文通过将智慧城市作为主要用例，评估了应对物联网数据挑战的不同机器学习方法。本研究的主要贡献是介绍了机器学习算法的分类，解释了如何将不同的技术应用于数据，以提取更高层次的信息。还将讨论机器学习在物联网数据分析方面的潜力和挑战。一个应用支持向量机（SVM）的奥胡斯智慧城市交通数据的用例，提出了一个更详细的探索。
英文摘要：
Rapid developments in hardware, software, and communication technologies have allowed the emergence of Internet-connected sensory devices that provide observation and data measurement from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As the numbers grow and technologies become more mature, the volume of data published will increase. Internet-connected devices technology, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interaction between the physical and cyber worlds. In addition to increased volume, the IoT generates Big Data characterized by velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this Big Data is the key to developing smart IoT applications. This article assesses the different machine learning methods that deal with the challenges in IoT data by considering smart cities as the main use case. The key contribution of this study is presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying Support Vector Machine (SVM) on Aarhus Smart City traffic data is presented for a more detailed exploration.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
