The Smart Cyber Ecosystem for Sustainable Development

10Impact of Deep Learning Techniques in IoT

M. Chandra Vadhana*, P. Shanthi Bala and Immanuel Zion Ramdinthara

Department of Computer Science, School of Engineering and Technology, Pondicherry University, Puducherry, India

Abstract

Deep Learning (DL) has significantly changed the way process of computing devices human-centric content such as speech, image recognition, and natural language processing. It is very useful for safety-critical applications such as driverless cars, aerospace, defense, medical research, and industrial automation. DL models exceed human-level performance in terms of accuracy. It is a subset of machine learning that performs end-to-end learning and can learn unsupervised data and also provides a very versatile, learnable framework for representing visual and linguistic information. DL plays a major role in IoT related services. It serves as an emerging solution for developing IoT systems enhanced with efficient, reliable, and effective DL models. The amalgamation of DL to the IoT environment makes the complex sensing and recognition tasks easier. It helps to automatically identify patterns and detect anomalies that are generated by IoT devices. This chapter discusses the impact of DL in the IoT environment.

Keywords: Internet of Things (IoT), deep learning (DL), neural network, convolutional neural network (CNN), big data, cloud, recurrent neural network (RNN), RFID

10.1 Introduction

Internet of Things (IoT) is a network of objects that are accessible ...