Abstract
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.
Highlights
The rise of Internet-of-Things (IoT) technology has brought prosperity to a myriad of emerging applications on various mobile and wireless platforms including smart phones [1], sensor networks [2], unmanned aerial vehicles (UAV) [3], [4], cognitive smart systems [5], and so on
In the following we present how deep learning is applied to traffic video analytics from the three perspectives: object detection, object tracking, and face recognition
In recent years, a major breakthrough has been made in convolutional neural networks [28]–[31] and the number of layers of Convolutional Neural network (CNN) models has been increasing from 5 to more than 200
Summary
The rise of Internet-of-Things (IoT) technology has brought prosperity to a myriad of emerging applications on various mobile and wireless platforms including smart phones [1], sensor networks [2], unmanned aerial vehicles (UAV) [3], [4], cognitive smart systems [5], and so on. Deep learning incorporates deeper neural network architectures, which is able to extract more complex hidden features (such as temporal and/or spatial dependencies) and characterize more intricate problems Different from those traditional simple learning methodologies, deep learning has more powerful capabilities in generalizing the complicated relationship of massive raw data in various IoT applications. We believe that it is the right time to review the existing literature and to motivate future research directions To this end, this article summarizes the up-to-date research progresses and trends on leveraging deep learning tools to empower IoT applications. We discuss the issues, challenges and future research directions for applying deep learning in IoT applications All these insights may motivate and inspire further developments in this promising field.
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