Book Image

Enterprise Internet of Things Handbook

By : Arvind Ravulavaru
Book Image

Enterprise Internet of Things Handbook

By: Arvind Ravulavaru

Overview of this book

There is a lot of work that is being done in the IoT domain and according to Forbes the global IoT market will grow from $157B in 2016 to $457B by 2020. This is an amazing market both in terms technology advancement as well as money. In this book, we will be covering five popular IoT platforms, namely, AWS IoT, Microsoft Azure IoT, Google IoT Core, IBM Watson IoT, and Kaa IoT middleware. You are going to build solutions that will use a Raspberry Pi 3, a DHT11 Temperature and humidity sensor, and a dashboard to visualize the sensor data in real-time. Furthermore, you will also explore various components of each of the platforms that are needed to achieve the desired solution. Besides building solutions, you will look at how Machine Learning and IoT go hand in hand and later design a simple predictive web service based on this concept. By the end of this book, you will be in a position to implement an IoT strategy best-fit for your organization
Table of Contents (12 chapters)

IoT technology stack

Before we start building IoT solutions using various out-of-the-box IoT platforms, I would like to talk about the various building blocks of an IoT solution. That includes smart devices, storage, API management, as well as intelligence.

This diagram represents an end-to-end IoT stack:

The stack consists of various layers that will be part of any typical IoT solution.

The bottommost layers in the stack are the smart devices or The Things. These communicate with the real world and gather the information around them to actuate things. And through the gateway, the smart devices talk to the device management software to keep them integrated with the remaining world.

With the help of the storage layer, the device management software persists the communications and data that arises between the smart devices and the API management layer. The API management layer is responsible for creating an interface between the device, its data, and the applications that want to control and manage these smart devices.

The applications here can be any entity that has the capability of consuming the exposed APIs and managing and monitoring smart devices. Applications here can include a mobile app, a web dashboard, a voice assistant, or an IVR service, to name a few.

On the other hand, rules engines and schedulers can also consume the data exposed by the APIs. Rules here can be simple actions when a certain type of threshold is reached and a schedule is where an action can be performed on a device at a certain configured time.

This data can also be used for analysis as well as for adding intelligence on top of the existing system. Technologies such as big data, machine learning, and artificial intelligence fall under this category.

On top of these layers is where third-party integrations take place.

In the subsequent sections, we will go into each of the layers and explore them further.