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IoT key technologies

The IoT is sometimes used as a synonym of big data, is sometimes confused with the cloud, and is sometimes linked to machine learning and artificial intelligence. All of these things are partially true:

IoT uses big data technology to store data
IoT is normally deployed on the cloud to improve scalability
IoT uses advanced analytics to process data

However, on the flip side, there's this to consider:

IoT is focused on a data stream, rather than having huge amounts (petabytes) of data storage
IoT can use on-premises solutions through virtualization technology
Machine learning on IoT is not as productive as simple threshold rules or physics-based analytics

These concepts have been highlighted by the The Eclipse Foundation's 2018 IoT survey. The following diagram shows the adoption of the cloud technologies by companies:

Eclipse's IoT survey— the technologies underlined in red are those that will be discussed in this book

The following shows IoT technology adoption from a storage point of view:

Eclipse's IoT survey— the technologies underlined in red are those that will be discussed in this book

In this book, we will explore the most common IoT cloud solutions, such as AWS, GCP, and Azure, and the most common OEM I-IoT platforms, such as Bosch IoT, Predix, and MindSphere, to provide state-of-the-art IoT technology. We will also look at other common open source technologies, including those in the following table:

These technologies can be used to build an IoT platform from scratch or to integrate with an existing one. We will also consider other commonly used commercial software in the industrial environment.

We will discover the new generation of edge computing and the edge gateway, and, finally, we will deal with machine learning and artificial intelligence. This journey is also the journey of the IoT from the cloud to the big revolution expected around 2020:

The waves of innovation in IoT