Most agree that the first generation of the Industrial Revolution began in the middle of the 18^th century. Let's go back in history to see how it led to the evolution of the IIoT today. The 18^th and 19^th centuries, which experienced the Industrial Revolution saw a transition from the manually intensive manufacturing processes to the mechanization of the manufacturing. This laid the foundation of the modern heavy industries. At the time, most people lived on farms and worked in agriculture. Factories were commonly located close to rivers and streams where they could be powered by water wheels, and there was usually much handwork involved. With the invention of the steam engine, factories could be located elsewhere. The power that was supplied to machinery by steam engines became more predictable, and more processes could be aided by machinery.

Great Britain experienced many technological innovations ranging from the first engine in 1712 by Thomas Newcomen to the steam engine in 1765 by James Watt to the first public railway line in 1825. The Industrial Revolution transformed manufacturing from the home and cottage industry level to a vastly more scalable level. With the introduction of railroads as a transportation alternative to river traffic and horse-driven carriages, faster travel between distant locations was enabled and provided a new means to deliver supplies to factories and products from them. This theme of decoupling the production facilities from the consumers can be seen in today's computing world where remote data centers can be decoupled from the information technology users. Over time, this Industrial Revolution led to a transition from human labor to the use of machines, spread over whole of Europe and to North America, leading to the industrialization of the world. Gradually, this led to consumerism as goods became available, accessible, and affordable.

The increased widespread availability of electricity through power grids and the invention of the assembly line in the first decades of the 1900s introduced the second generation of the Industrial Revolution. Once again, power became more predictable and the amount of space required for power generation in factories was reduced. Production became more optimized through assembly lines, and workers assumed new specialized roles. Motorized vehicles also appeared for the delivery of supplies and transporting finished products, thus enabling more variation in factory locations. We began an age of mass production as well as mass merchandising, which resulted in the creation of many additional, new kinds of job.

In the third generation, business computing was introduced and efficiencies were greatly improved. Mainframe computers became widely available with subsequent pricing adjustments, making them more affordable and hence more widely adopted in the 1960s. Still cheaper minicomputers and then personal computers followed. The Internet was in common usage for networking within companies and across the world by the 1990s.

The Internet evolved from a way for the military to connect and communicate and appeared in universities and then mainstream companies. The mid 1990s saw the transition from the military's Advanced Research Projects Agency Network (ARPANet) to the consumer Internet. In this wave, computers and servers connected across the world and then provided an information super highway for the people. This revolutionized how people interacted with each other and with the businesses leading to the growth of e-commerce and social media. New leaders emerged in this era, starting with Information Technology (IT) system providers, and companies such as Amazon, which started with online sale of books and went onto become a general-purpose e-commerce platform. Likewise, on the human interaction front, emails became mainstream and more interactive and rich multi-media evolved on the web. This led to the rise of Myspace, Facebook, Twitter, and similar social media platforms to essentially connect the people across the world. We refer to this as the consumer Internet.

Computing also became more accessible through improved software development tools and through business applications and tools that provided increasingly more intuitive user interfaces. Some refer to this as the beginning of the information age as line of business users could access and manipulate their data to measure and optimize their business activities.

While the consumer Internet focused on connectivity between businesses, consumers, the IT systems, and the computing devices such as servers, PC's, laptops, and emerging mobile devices, it largely ignored the machines from the Industrial Revolution. This led to a great divide between the machines for industrial operations and the traditional IT systems and set the stage for the fourth wave that we call the Industrial Internet Revolution.

The Industrial Internet can be defined as the connecting of industrial-grade machines and devices to networked computing devices with the goal of collecting the diverse data originating both inside the machine and the surrounding environment and processing or analyzing this data for meaningful outcomes. Such data originates in various forms and is often referred to as big data. The systematic organization and analysis of this data is referred to as big data analytics for industrial outcomes.

The Industrial Internet and IIoT are the industrial flavor of the IoT. While IoT refers to any physical object or thing connected to a network and the Internet, IIoT focuses on scenarios where the connected objects are primarily industrial in nature (such as manufacturing assembly lines, power generation equipment, or mass transportation vehicles). Thus, Industrial Internet is often used interchangeably with IIoT. There are three Ps important to an industry:

• Products (machines and assets)
• Processes (assembly lines and supply chain)
• People (human stakeholders)

The following illustration captures the interaction of these three Ps:

Industrial machines and assets have a long life, especially when compared to many consumer devices. The following table serves as a reference to highlight the difference of scale in usable life comparing various industrial assets to a smartphone:

Due to the long life and cost of ownership of industrial machines, it is important to provide ways to protect the investment in these machines over time. Thus, the optimization of field maintenance services is an integral part of the Industrial Internet. Service execution and service delivery platforms and applications are within the realm of the Industrial Internet architects, and this book will provide coverage to it.

The long life of industrial assets leads to two terms often used in the context of Industrial Internet solutions: greenfield and brownfield applications. A greenfield project refers to a scenario where a company decides to build a new infrastructure since it offers the maximum design flexibility and efficiency to meet a project's needs (an existing infrastructure limits the ability to change by its present design). From the Industrial Internet architecture perspective, the new infrastructure can add sensors to collect relevant data.

Brownfield projects leverage infrastructure that is already in use. The costs of starting up are usually greatly reduced with this approach, but it can be more difficult to modernize the infrastructure and incorporate the addition of sensors. Construction and commissioning times can be minimized using this approach. For Industrial Internet projects, brownfield systems can be retrofitted by adding external sensors to collect data. For example, external acoustic sensors might be added to the body of air compressors in a factory to do the harmonic analysis and determine air leaks in a brownfield project. Air leaks can cause wasted electricity in manufacturing plants where compressors are used to drive several pneumatic tools.

Some of the concepts we associate with the Industrial Internet today began to mature in the last few years. For example, in a world before widely available smart sensors, oil and gas exploration companies brought computers to the exploration sites, processed the data locally in relational databases, and transmitted the processed data and their conclusions back to their headquarters. Some referred to this as early edge computing on the remote computers. The following diagram reflects this type of deployment:

Data warehouses and data marts became common in most businesses. Batch-fed by Online Transaction Processing (OLTP) systems, they became the place to store historical data used to report on current trends and compare current data with past data through business intelligence tools. Of course, this footprint remains common today.

Predictive algorithms were also developed, tested, and deployed with increasing rapidity in certain industries and gained wider adoption over time. Some early use cases included understanding financial market investment strategies and insurance risk, and the prediction of the likely quality of expensive manufacturing processes to better optimize the production.

Each generation became shorter. Moving from the first generation of the Industrial Revolution to the next was a matter of centuries, but the subsequent generations took half the time of the previous change. This implies that future generations may come at a faster pace, and while we are embracing the Industrial Internet, we need to be prepared for the possible next generations as well.