Chapter 8. Disruptions in IoT

With the Internet of Things paradigm sensing increasing penetration in every industrial vertical, we have witnessed phenomenal disruptions in the IoT fraternity. Success stories are shooting up in every industrial vertical by demonstrating the value and potential of IoT. Artificial intelligence, machine learning, deep learning, robotics, genomics, cognitive computing, fog computing, edge computing, smart factory, and a plethora of other disruptions have emerged with IoT. We have directly or indirectly benefited from these disruptions while leveraging the innovations in technology used in our daily chores. As time progresses, we can affirmatively expect to scale this even better.

Connected assets and connected operations have now become a reality, and we will see disruptions in IoT with the convergence of innovation from multiple disciplines. To name a few, the increasing volume of data has fostered the growth of deep learning in IoT, edge computing or fog computing...

Edge/fog computing

The topic of fog computing has been getting a lot of traction in recent years. The concept has been in the research and experimental phase for quite some time, but with the recent growth of IoT, edge computing has starting evolving from the "Innovation Trigger" phase to the "Peak of inflated expectation" phase (referring to Gartner's Hype cycle). The edge computing concept got such phenomenal traction that Cisco coined the term fog computing as an inspiration from the legacy architecture of cloud computing.

Let's understand the fog computing concept in layman terms.

Edge computing/fog computing is an architecture where the computing of data, applications, and services is pushed away from the centralized cloud to the logical extremes of the network, that is, the edge. This approach requires leveraging resources that may not be continuously connected to a network such as laptops, smartphones, tablets, home appliances, manufacturing industrial machines, sensors, and so on...

Cognitive Computing - Disrupting intelligence from unstructured data

As we see evolution in connectivity, computing, and technologies, we see disruptions continuing in the industry. IoT has been a blessed recipient of many disruptions due to its charm. We have lately seen the evolution of cognitive computing in the IoT ecosystem.

Cognitive computing can be defined as the third era of computing where it solves problems that have increased complexity and uncertainty, that is, the human kind of a problem. To solve such problems, the systems have been designed to mimic the way the humans solve a problem.

So on a general note, how do we learn? Humans learn from experiences. At any point of time, there is a flow of information that we consume from the world. We learn how to react to new situations based on our historic learnings; we teach ourselves how to learn. The simplest evidence for this can be, say you are asked to solve a puzzle that you have never heard before. How do you solve it? You think...

Next generation robotics and genomics

With increasing innovations in the industry due to IoT and other fields, every field senses a new growth dimension in some way or other. With IoT booming, we saw edge computing picking up in the industry. Edge computing played a pivotal role in industrial IoT, enhancing a machine's operational efficiency and adding various other benefits. Edge computing not only fostered the innovations in industrial IoT, but also cemented the foundations for cognitive computing. Cognitive computing solutions boosted with the simplified architecture of edge, providing a scale as an easier and hassle-free dimension aided in phenomenal improvements to the robotics industry.

Robotics – A bright future with IoT, Machine Learning, Edge & Cognitive Computing

Today, with cognitive computing, machine learning, edge computing, and IoT, we have the robotics industry shaping up into a state-of-the-art technology. Robotics have been in place and been used extensively in the manufacturing...

Autonomous cars

The final topic that we will discuss in this chapter for disruptions in IoT is autonomous cars. Autonomous cars have been surfacing the technology innovations for quite some time but are yet to hit mainstream production. Most cars that have some sort of autonomous feature are still limited only to the flagship vehicles from the premium carmakers. Google's self-driving cars have been making news for a while and have seen quite a hefty progress in the accuracy of self-driving. Autonomous cars are defined at the cusp of innovation in the industry. It combines learnings from IoT, artificial intelligence, machine learning, cognitive computing, and edge computing and delivers a world-class solution that has been fancied for a really long time. We'll understand a few important aspects about autonomous cars in order to understand the concept. We'll first touch base on the vision and inspiration for which autonomous cars had been developed. We'll study about the miniscule forms of...

Privacy and security in IoT

We have studied in brief about the disruptions in IoT and have explored how IoT has opened up various new areas for innovation. We saw how edge computing, cognitive computing, machine learning, artificial intelligence, and other disruptions have fostered new areas like autonomous cars, next-generation robotics, and genomics, but we have missed studying one vital dimension in IoT-privacy and security of the data. With great detail about the data that can help us do wonders come great threats of security and privacy. In IoT, the security and privacy requirements are paramount. There cannot be any compromise in the IoT ecosystem to leverage the benefits to mankind. A small loophole is enough to cause a disaster to large organizations, governments, and individual citizens.

Exposing the data in an IoT system will make the system vulnerable to cause disasters to mankind. A user's medical data and digital data is extremely sensitive and confidential and cannot be leveraged...

Summary

In this chapter, we studied the disruptions in IoT. We studied how the growth of IoT has emerged innovations in different fields and how other fields have leveraged IoT directly or indirectly to trigger disruptions in the market. We explored the fog or edge computing model and saw how the IoT infrastructure can be scaled efficiently while still keeping it a viable solution. To study the fog computing model in detail, we explored a hypothetical use case similar to the manufacturing use case studied in earlier. We saw how connected devices or assets can be designed to become state-of-the-art smart devices where intelligence is pushed to the logical extremes of the network, promoting quick and intelligent self-decisions to improve an outcome.

We explored cognitive computing, a fairly new but very promising and interesting area emerged from the convergence of artificial intelligence, IoT, and edge computing. We saw how machines can be designed to learn on their own and solve a human-like...