Book Image

Hands-On Deep Learning for IoT

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

Hands-On Deep Learning for IoT

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)
Free Chapter
1
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

Use case one – image-based automated fault detection

Public assets (such as roads, public buildings, and tourist places) in a city are heterogeneous and distributed within the city. Most cities in the world face challenges in monitoring, fault detection, and reporting these assets. For example, in many UK cities, citizens often report faults, but the accuracy and efficiency of the reporting is an issue in many cases. In a smart city, these assets can be monitored, and their faults can be detected and reported through an IoT application. For example, a vehicle (such as a city council vehicle) attached with one or more sensors (such as a camera or a mic) can be used for the road fault monitoring and detection.

Roads are important assets in a city, and they have many faults. Potholes, bumps, and road roughness are some of the most frustrating hazards and anomalies experienced...