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The End-to-End Life Cycle of the IoT

By enabling easy access it, and interaction with, a wide variety of physical devices and their environments, the Internet of Things (IoT) will foster the development of various applications in various domains, such as health and medical care, intelligent energy management and smart grids, transportation, traffic management, and more. These applications will generate big and real-time/streaming data, which will require big data analysis tools, including advanced machine learning, that is, deep learning (DL), to extract useful information and make informed decisions. We need to understand the end-to-end (E2E) life cycle of the IoT and its different components in order to apply advanced machine learning techniques on the generated data of IoT applications.

In this chapter, we will discuss the E2E life cycle of the IoT and its related concepts...

Key benefits

Understand how deep learning facilitates fast and accurate analytics in IoT

Build intelligent voice and speech recognition apps in TensorFlow and Chainer

Analyze IoT data for making automated decisions and efficient predictions

Description

Artificial Intelligence is growing quickly, which is driven by advancements in neural networks(NN) and deep learning (DL). With an increase in investments in smart cities, smart healthcare, and industrial Internet of Things (IoT), commercialization of IoT will soon be at peak in which massive amounts of data generated by IoT devices need to be processed at scale. Hands-On Deep Learning for IoT will provide deeper insights into IoT data, which will start by introducing how DL fits into the context of making IoT applications smarter. It then covers how to build deep architectures using TensorFlow, Keras, and Chainer for IoT. You’ll learn how to train convolutional neural networks(CNN) to develop applications for image-based road faults detection and smart garbage separation, followed by implementing voice-initiated smart light control and home access mechanisms powered by recurrent neural networks(RNN). You’ll master IoT applications for indoor localization, predictive maintenance, and locating equipment in a large hospital using autoencoders, DeepFi, and LSTM networks. Furthermore, you’ll learn IoT application development for healthcare with IoT security enhanced. By the end of this book, you will have sufficient knowledge need to use deep learning efficiently to power your IoT-based applications for smarter decision making.

Who is this book for?

If you’re an IoT developer, data scientist, or deep learning enthusiast who wants to apply deep learning techniques to build smart IoT applications, this book is for you. Familiarity with machine learning, a basic understanding of the IoT concepts, and some experience in Python programming will help you get the most out of this book.

What you will learn

Get acquainted with different neural network architectures and their suitability in IoT

Understand how deep learning can improve the predictive power in your IoT solutions

Capture and process streaming data for predictive maintenance

Select optimal frameworks for image recognition and indoor localization

Analyze voice data for speech recognition in IoT applications

Develop deep learning-based IoT solutions for healthcare

Enhance security in your IoT solutions

Visualize analyzed data to uncover insights and perform accurate predictions

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