Hands-On Deep Learning for IoT

Train neural network models to develop intelligent IoT applications

Md. Rezaul Karim

• Publisher: Packt Publishing
• Year: 2019
• Print ISBN: 9781789616132
• eBook ISBN: 9781789616064
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Implement popular deep learning techniques to make your IoT applications smarter

Key Features

• 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

Book 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.

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

Who this book is 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.

Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks

In this section, we will have an overview of IoT ecosystems, the key characteristics of IoT data (that is, real-time big data). We explain why analytics is necessary for data and why deep learning (DL) is important for analysis. We will also look into various techniques, their models and architectures, and their suitability in the IoT applications domain.

The following chapters are included in this section:

• Chapter 1, End-to-End Life Cycle of IoT
• Chapter 2, Deep Learning Architectures for IoT

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 and components. We will explore its key characteristics and the IoT data issues that demand the use of DL in IoT. We will cover the following topics:

• The E2E life cycle of the IoT:
  • IoT application domains:
    • The importance of analytics in IoT
    • The motivation to use DL in IoT data analytics
• The key characteristics and requirements of IoT data

The E2E life cycle of the IoT

Different organizations and industries describe IoT differently. One way of defining it simply and tangibly is as a network of smart objects, which connects the physical and digital world together. Examining the E2E life cycle of the IoT solution or, more generally, of the IoT ecosystem, will help us to understand it further and show us how it is applicable to machine learning and DL.

Similar to the definition of IoT, there is no single consensus on the E2E life cycle or the IoT architecture that is agreed universally. Different architectures or layers have been proposed by different researchers. The most commonly proposed options are the three and five-layer life cycles or architectures, as shown in the following diagram:

In the preceding diagram, (a) presents a three-layer IoT life cycle or architecture, and (b) presents a five-layer IoT life cycle or architecture.

The three-layer E2E IoT life cycle

This is the most basic and widely used IoT life cycle for IoT solutions. It consists of three layers: the perception, network, and application layers. These can be described as follows:

• The perception layer: This is the physical layer or sensing layer, which includes things or devices that have sensors to gather information about their environments. As shown in the following diagram, the perception layer of an E2E life cycle of the IoT solution in healthcare consists of patients, hospital beds, and wheelchairs that are deployed with sensors.
• The network layer: A network is responsible for connecting to other smart things, network devices, and servers. It is also responsible for transmitting and processing sensor data.
• The application layer: This layer is responsible for delivering application-specific services to users, based on the data from the sensor. It defines various applications in which IoT can be deployed, for example, smart homes, smart cities, and connected health.

The following diagram presents a three-layer E2E life IOT cycle in healthcare:

The three-layer E2E IoT life cycle or architecture defines the key ideas of IoT, but it may not be enough for research and development, as these often deal with the finer aspects of IoT. This is why other life cycles or architectures, such as the five-layer life cycle, have been proposed.

The five-layer IoT E2E life cycle

A five-layer IoT life cycle consists of the perception, transport, processing, application, and business layers. The role of the perception and application layers is the same as in the three-layer architecture. We outline the function of the remaining three layers as follows:

• The transport layer: This is similar to the network layer of the three-layer life cycle. It transfers the data gathered in the perception layer to the processing layer and vice versa through networks such as wireless, 3G, LAN, Bluetooth, RFID, and NFC.
• The processing layer: This is also known as the middleware layer. It stores, analyzes, and processes huge amounts of data that comes from the transport layer. It can manage and provide a diverse set of services to the lower layers. It employs many technologies, such as databases, cloud computing, and big data processing modules.
• The business layer: This layer manages the whole IoT system, including applications, business and profit models, and user privacy.

IoT system architectures

Understanding the architecture of the IoT system is important for developing an application. It is also important to consider our data processing requirements in different computing platform levels, including the fog level and the cloud level. Considering the criticality and latency-sensitiveness of many IoT applications (such as an IoT solution for the healthcare domain, as shown in the previous diagram), fog computing is essential for these applications. The following diagram, very briefly, presents how fog computing works:

As we can see in the preceding diagram, in fog computing, a thing's (such as a car's) data does not move to the cloud for processing. In this way, fog computing addresses many challenges (such as high latency, downtime, security, privacy, and trust) faced by the cloud in IoT and offers many benefits, such as location awareness, low latency, support for mobility, real-time interactions, scalability, and business agility. The following diagram presents the protocol layer-wise architecture of fog computing:

As shown in the preceding diagram, the architecture of fog computing or fog computing with IoT consists of six layers: physical and virtualization, monitoring, preprocessing, temporary storage, security, and transport. Notably, the preprocessing layer performs data management tasks by essentially analyzing, filtering, and trimming collected data from physical or virtual sensors.

IoT application domains

By enabling easy access to, and interaction with, a wide variety of physical devices or things such as vehicles, machines, medical sensors, and more, IoT facilitates the development of applications in many different domains. The following diagram highlights the key application domains of IoT:

These include healthcare, industrial automation (that is, Industry 4.0), energy management and smart grids, transportation, smart infrastructure (such as the smart home and the smart city), retail, and many other areas that will transform our lives and societies for the better. These applications will have a global economic impact of $4 to $11 trillion per year by 2025. The key contributors (in order of their predicted contribution) of this quantity of money include the following:

• Factories or industries, including operation management and predictive maintenance
• Cities, including public safety, health, traffic control, and resource management
• Healthcare, including monitoring and managing illnesses and improving wellness
• Retail, including self-checkouts and inventory management
• Energy, including the smart grid

The tremendous demand for these applications implies the incredible and steep growth of IoT services and the big data that they generate.

The importance of analytics in IoT

The use of IoT in various application domains will only be effective if those applications can extract some business value from the data generated and collected by IoT devices. In this context, analysis of IoT data is essential in IoT solutions. Gartner identified IoT analytics as one of the two top technologies used in IoT.

IoT analytics is the application of data analysis tools and procedures to unlocking insights fro...

Table of contents

• Title Page
• Copyright and Credits
• About Packt
• Contributors
• Preface
• Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks
• The End-to-End Life Cycle of the IoT
• Deep Learning Architectures for IoT
• Section 2: Hands-On Deep Learning Application Development for IoT
• Image Recognition in IoT
• Audio/Speech/Voice Recognition in IoT
• Indoor Localization in IoT
• Physiological and Psychological State Detection in IoT
• IoT Security
• Section 3: Advanced Aspects and Analytics in IoT
• Predictive Maintenance for IoT
• Deep Learning in Healthcare IoT
• What's Next - Wrapping Up and Future Directions
• Other Books You May Enjoy