Book Image

Artificial Intelligence for IoT Cookbook

By : Michael Roshak
Book Image

Artificial Intelligence for IoT Cookbook

By: Michael Roshak

Overview of this book

Artificial intelligence (AI) is rapidly finding practical applications across a wide variety of industry verticals, and the Internet of Things (IoT) is one of them. Developers are looking for ways to make IoT devices smarter and to make users’ lives easier. With this AI cookbook, you’ll be able to implement smart analytics using IoT data to gain insights, predict outcomes, and make informed decisions, along with covering advanced AI techniques that facilitate analytics and learning in various IoT applications. Using a recipe-based approach, the book will take you through essential processes such as data collection, data analysis, modeling, statistics and monitoring, and deployment. You’ll use real-life datasets from smart homes, industrial IoT, and smart devices to train and evaluate simple to complex models and make predictions using trained models. Later chapters will take you through the key challenges faced while implementing machine learning, deep learning, and other AI techniques, such as natural language processing (NLP), computer vision, and embedded machine learning for building smart IoT systems. In addition to this, you’ll learn how to deploy models and improve their performance with ease. By the end of this book, you’ll be able to package and deploy end-to-end AI apps and apply best practice solutions to common IoT problems.
Table of Contents (11 chapters)

Deploying mobile models

Many IoT scenarios require that you have a graphical user interface; that is, a high level of compute, Bluetooth, and Wi-Fi and a cellular network. Most modern cell phones have these. An inexpensive IoT device can talk to an app on a smartphone via Bluetooth and use that app to perform ML and talk to the cloud.

Using cell phones can cut the time to market for IoT devices. These devices can use a secure and easily updatable app to send data to the cloud. The portability of cell phones is an appeal but also a drawback. Having a device constantly communicating with the cloud can drain a cell phone's battery so that it lasts for as little as 8 hours. Because of this, companies often look to edge processing to perform compute tasks such as machine learning. This allows the device to send data less frequently.

How cell phones are used for IoT is ubiquitous. Companies such as Fitbit and Tile use low-power Bluetooth Low Energy (BLE) to send data to consumer cell phones...