A Survey on Deep Learning Empowered IoT Applications
A Survey on Deep Learning Empowered IoT Applications
复制标题
深度学习赋能物联网应用调查
DOI：
10.1109/access.2019.2958962
复制
发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Liu, Jiangchuan
中科院分区：
文献类型：
--
作者：
Ma, Xiaoqiang;Yao, Tai;Liu, Jiangchuan
物联网（IoT）被广泛认为是未来互联网的关键组成部分，因此近年来引起了人们的广泛关注。物联网由数十亿个智能、可通信的“物”组成，进一步扩展了世界与物理和虚拟实体的边界。这些无处不在的智能物每天都会产生海量数据，对各种智能移动设备上的快速数据分析提出了迫切要求。幸运的是，深度学习最近的突破使我们能够以优雅的方式解决这个问题。深度模型可以导出来处理海量传感器数据，并为智能设备上的各种物联网应用快速高效地学习底层特征。在本文中，我们回顾了有关将深度学习应用于各种智能设备的文献。我们的目标是从不同的角度深入探讨如何应用深度学习工具来增强智能医疗、智能家居、智能交通和智能工业等四个代表性领域的物联网应用。我们的主要目标是将深度学习和物联网这两个学科无缝融合，从而产生一系列物联网应用的新设计，例如健康监测、疾病分析、室内定位、智能控制、家庭机器人、交通预测、交通监控、自动驾驶和制造检查。利用深度学习来增强物联网应用程序的能力，这可能会激励和激发这一前景广阔的领域的进一步发展。
The Internet of Things (IoT) is widely regarded as a key component of the Internet of the future and thereby has drawn significant interests in recent years. IoT consists of billions of intelligent and communicating "things'', which further extend borders of the world with physical and virtual entities. Such ubiquitous smart things produce massive data every day, posing urgent demands on quick data analysis on various smart mobile devices. Fortunately, the recent breakthroughs in deep learning have enabled us to address the problem in an elegant way. Deep models can be exported to process massive sensor data and learn underlying features quickly and efficiently for various IoT applications on smart devices. In this article, we survey the literature on leveraging deep learning to various IoT applications. We aim to give insights on how deep learning tools can be applied from diverse perspectives to empower IoT applications in four representative domains, including smart healthcare, smart home, smart transportation, and smart industry. A main thrust is to seamlessly merge the two disciplines of deep learning and IoT, resulting in a wide-range of new designs in IoT applications, such as health monitoring, disease analysis, indoor localization, intelligent control, home robotics, traffic prediction, traffic monitoring, autonomous driving, and manufacture inspection. We also discuss a set of issues, challenges, and future research directions that leverage deep learning to empower IoT applications, which may motivate and inspire further developments in this promising field.