Hands-On Deep Learning for IoT

By : Dr. Mohammad Abdur Razzaque, Md. Rezaul Karim

4 (1)

Buy this Book

Hands-On Deep Learning for IoT

4 (1)

By: Dr. Mohammad Abdur Razzaque, Md. Rezaul Karim

Buy this Book

Overview of this book

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.

Preface

Who this book is for

What this book covers

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

The End-to-End Life Cycle of the IoT

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

Summary

Reference

Deep Learning Architectures for IoT

A soft introduction to ML

ML tasks

Delving into DL

Artificial neural networks

Neural network architectures

DL frameworks and cloud platforms for IoT

Summary

Section 2: Hands-On Deep Learning Application Development for IoT

Image Recognition in IoT

IoT applications and image recognition

Use case one – image-based automated fault detection

Use case two – image-based smart solid waste separation

Transfer learning for image recognition in IoT

CNNs for image recognition in IoT applications

Collecting data for use case one

Collecting data for use case two

Data pre-processing

Models training

Evaluating models

Summary

References

Audio/Speech/Voice Recognition in IoT

Speech/voice recognition for IoT

Use case one – voice-controlled smart light

Use case two – voice-controlled home access

DL for sound/audio recognition in IoT

CNNs and transfer learning for speech recognition in IoT applications

Collecting data

Data preprocessing

Models training

Evaluating models

Summary

References

Indoor Localization in IoT

An overview of indoor localization

DL-based indoor localization for IoT

Example – Indoor localization with Wi-Fi fingerprinting

Deployment techniques

Summary

Physiological and Psychological State Detection in IoT

IoT-based human physiological and psychological state detection

Use case one – remote progress monitoring of physiotherapy

Use case two — IoT-based smart classroom

Deep learning for human activity and emotion detection in IoT

LSTM, CNNs, and transfer learning for HAR/FER in IoT applications

Data collection

Data exploration

Data preprocessing

Model training

Model evaluation

Summary

References

IoT Security

Security attacks in IoT and detections

Use case one: intelligent host intrusion detection in IoT

Use case two: traffic-based intelligent network intrusion detection in IoT

DL for IoT security incident detection

Data collection

Data preprocessing

Model training

Model evaluation

Summary

References

Section 3: Advanced Aspects and Analytics in IoT

Predictive Maintenance for IoT

Predictive maintenance for IoT

Example – PM for an aircraft gas turbine engine

DL for predicting RLU

FAQs

Summary

Deep Learning in Healthcare IoT

IoT in healthcare

Use case one – remote management of chronic disease

Use case two – IoT for acne detection and care

DL for healthcare

Data collection

Data exploration

Data preprocessing

Model training

Model evaluations

Summary

References

What's Next - Wrapping Up and Future Directions

What we have covered in this book?

Deployment challenges of DL solutions in resource-constrained IoT devices

Existing solutions to support DL in resource-constrained IoT devices

Potential future solutions

Summary

References

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Models training

As we mentioned earlier, we are using transfer learning that does not require training from scratch; retraining of the models with a new dataset will sufficiently work in many cases. We retrained two popular architectures or models of CNN, namely Incentive V3 and Mobilenet V1, on a desktop computer, which is replicating the city council’s server. In both models, it took less than an hour to retrain the models, which is an advantage of the transfer learning approach. We need to understand the list of key arguments before running the retrain.pyfile, which is in the code folder. If we type in our Terminal (in Linux or macOS) or Command Prompt (Windows) python retrain.py -h, we shall see a window like the following screenshot with additional information (that is, an overview of each argument). The compulsory argument is the image directory, and it is one of...