Book Image

Smarter Decisions - The Intersection of Internet of Things and Decision Science

By : Jojo Moolayil
Book Image

Smarter Decisions - The Intersection of Internet of Things and Decision Science

By: Jojo Moolayil

Overview of this book

With an increasing number of devices getting connected to the Internet, massive amounts of data are being generated that can be used for analysis. This book helps you to understand Internet of Things in depth and decision science, and solve business use cases. With IoT, the frequency and impact of the problem is huge. Addressing a problem with such a huge impact requires a very structured approach. The entire journey of addressing the problem by defining it, designing the solution, and executing it using decision science is articulated in this book through engaging and easy-to-understand business use cases. You will get a detailed understanding of IoT, decision science, and the art of solving a business problem in IoT through decision science. By the end of this book, you’ll have an understanding of the complex aspects of decision making in IoT and will be able to take that knowledge with you onto whatever project calls for it
Table of Contents (15 chapters)
Smarter Decisions – The Intersection of Internet of Things and Decision Science
Credits
About the Author
About the Reviewer
eBooks, discount offers, and more
Preface

Demystifying M2M, IoT, IIoT, and IoE


So now that we have a general understanding about what is IoT, lets try to understand how it all started. A few questions that we will try to understand are: Is IoT very new in the market?, When did this start?, How did this start?, Whats the difference between M2M, IoT, IoE, and all those different names?, and so on. If we try to understand the fundamentals of IoT, that is, machines or devices connected to each other in a network, which isn't something really new and radically challenging, then what is this buzz all about?

The buzz about machines talking to each other started long before most of us thought of it, and back then it was called Machine to Machine Data. In early 1950, a lot of machinery deployed for aerospace and military operations required automated communication and remote access for service and maintenance. Telemetry was where it all started. It is a process in which a highly automated communication was established from which data is collected by making measurements at remote or inaccessible geographical areas and then sent to a receiver through a cellular or wired network where it was monitored for further actions. To understand this better, lets take an example of a manned space shuttle sent for space exploration. A huge number of sensors are installed in such a space shuttle to monitor the physical condition of astronauts, the environment, and also the condition of the space shuttle. The data collected through these sensors is then sent back to the substation located on Earth, where a team would use this data to analyze and take further actions. During the same time, industrial revolution peaked and a huge number of machines were deployed in various industries. Some of these industries where failures could be catastrophic also saw the rise in machine-to-machine communication and remote monitoring:

Telemetry

Thus, machine-to-machine data a.k.a. M2M was born and mainly through telemetry. Unfortunately, it didn't scale to the extent that it was supposed to and this was largely because of the time it was developed in. Back then, cellular connectivity was not widespread and affordable, and installing sensors and developing the infrastructure to gather data from them was a very expensive deal. Therefore, only a small chunk of business and military use cases leveraged this.

As time passed, a lot of changes happened. The Internet was born and flourished exponentially. The number of devices that got connected to the Internet was colossal. Computing power, storage capacities, and communication and technology infrastructure scaled massively. Additionally, the need to connect devices to other devices evolved, and the cost of setting up infrastructure for this became very affordable and agile. Thus came the IoT. The major difference between M2M and IoT initially was that the latter used the Internet (IPV4/6) as the medium whereas the former used cellular or wired connection for communication. However, this was mainly because of the time they evolved in. Today, heavy engineering industries have machinery deployed that communicate over the IPV4/6 network and is called Industrial IoT or sometimes M2M. The difference between the two is bare minimum and there are enough cases where both are used interchangeably. Therefore, even though M2M was actually the ancestor of IoT, today both are pretty much the same. M2M or IIoT are nowadays aggressively used to market IoT disruptions in the industrial sector.

IoE or Internet of Everything was a term that surfaced on the media and Internet very recently. The term was coined by Cisco with a very intuitive definition. It emphasizes Humans as one dimension in the ecosystem. It is a more organized way of defining IoT. The IoE has logically broken down the IoT ecosystem into smaller components and simplified the ecosystem in an innovative way that was very much essential. IoE divides its ecosystem into four logical units as follows:

  • People

  • Processes

  • Data

  • Things

Built on the foundation of IoT, IoE is defined as The networked connection of People, Data, Processes, and Things. Overall, all these different terms in the IoT fraternity have more similarities than differences and, at the core, they are the same, that is, devices connecting to each other over a network. The names are then stylized to give a more intrinsic connotation of the business they refer to, such as Industrial IoT and Machine to Machine for (B2B) heavy engineering, manufacturing and energy verticals, Consumer IoT for the B2C industries, and so on.