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Hands-On Deep Learning for IoT

Hands-On Deep Learning for IoT

By : Dr. Mohammad Abdur Razzaque, Md. Rezaul Karim
4 (1)
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Hands-On Deep Learning for IoT

Hands-On Deep Learning for IoT

4 (1)
By: Dr. Mohammad Abdur Razzaque, Md. Rezaul Karim

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.
Table of Contents (15 chapters)
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Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks
4
Section 2: Hands-On Deep Learning Application Development for IoT
10
Section 3: Advanced Aspects and Analytics in IoT

IoT Security

The use of IoT is growing at a dangerously fast pace, and both researchers and industries have estimated that, the number of active wirelessly connected devices will exceed 20 billion. This exponential growth of IoT devices is increasing the risks to our lives and property, as well as to the entire IT industry. To have more connected devices means more attack vectors, and more opportunities for hackers to exploit. In this context, secure IoT is not only essential for its applications, but also for the rest of the IT industry.

In IoT security solutions, networks and devices can be viewed as either signature-based or behavior-based. Behavior-based solutions, such as anomaly detection, are preferable in IoT as preparing and maintaining signatures of dynamic and unknown IoT attacks is very difficult. Similarly to human behavior analysis, deep learning (DL)/machine...

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