How Big is the Internet of Things?

The Internet of Things (IoT) is emerging as an unprecedented opportunity for many players in the communications, information technology, and (consumer) electronics industries. Although definitions of ‘IoT’, application scopes and growth forecasts strongly diverge – some analysts estimate the number of connected devices by 2020 around 20 billion connected devices, while the most bullish forecasts exceed 100 billion things – all sources agree that the IoT is going to be ‘huge.’ Estimates about the economic impact of the IoT show spectacular numbers too. While IDC is talking about $1.7 Trillion IoT spending in 2020, McKinsey believes that the market for IoT could reach $11.1 trillion by 2025.


In this article we discuss the value of the Internet of Things. We take a non-crystal ball approach to measure IoT’s tangible and intangible value, and make an estimate of its long term value based on real market data. Starting from two established ICT industry principles, Moore’s law and Metcalfe’s law, we explore IoT’s evolution curve in terms of performance, cost and value.

By introducing Geoffey Moore’s chasm theory, and Jeremy Rifkin’s zero marginal cost model, we dig into IoT’s challenges and opportunities, and its social and economic potential.

Then, we analyze the hypothesis in quantitative terms. When numbers are applied to the hypothesis we gain insights into why the IoT estimates are so lofty, and how the theorems interact to enable a revolution in the way we go about our daily activities in the near future.

Finally, we explain the business rationale for a standards-based horizontal IoT approach. Deploying a common network and platform infrastructure, on which a broad variety of devices and applications share platform functions and data, will fuel the exponential growth of the Internet of Things.

Moore’s law has been driving IoT evolution

In 1965, Intel co-founder Gordon Moore predicted that transistor density (and thus the performance) of microprocessors would double every 2 years. Take for example today’s iPhone 6s, which is 3.5 times faster than the iPhone 1, and has 8X more RAM memory, while its (on contract) price is 40% less than the first generation 7 years ago.

Figure 1: Moore’s law

Note: “doubling every 2 years” suggests a parabola-shaped curve, but Moore’s growth function is almost always represented in a straight line ― complemented by a logarithmic scale on the Y-axis.

Although recent articles (such as the one in Nature) and chip-maker announcements (like the one by Intel) are suggesting that the evolution of silicon technology may be reaching its physical limit, Moore’s prediction has been driving Information and Communications Technology (ICT) evolution for the past 5 decades.


Figure 2: processor evolution is following Moore’s law

Interviewed by the New York Times on the 50th anniversary of the law that was named after him, Gordon Moore admitted his amazement about the preciseness of his 1965 forecast:

“The original prediction was to look at 10 years, which I thought was a stretch. This was going from about 60 elements on an integrated circuit to 60,000 — a thousandfold extrapolation over 10 years. I thought that was pretty wild. The fact that something similar is going on for 50 years is truly amazing. You know, there were all kinds of barriers we could always see that [were] going to prevent taking the next step, and somehow or other, as we got closer, the engineers had figured out ways around these. But someday it has to stop. No exponential like this goes on forever.”

Mobile devices and machine-to-machine (M2M) communication modules are following similar performance and cost curves. According to Machina Research, NB-IoT efforts will result in sub-10$ “Category Zero” LTE modules by 2018. It has to be noted, however, that the total cost of ownership (TCO) of M2M connectivity depends on more than just the hardware module element, which is only about 15% of the cost. It also includes design, integration and device-network certification costs.

Metcalfe’s law adds value to the equation

Several years after Gordon Moore’s famous observation, another technology pioneer, 3Com co-founder Bob Metcalfe, stated that the value of a network grows by the square of the number of network nodes (or devices, or applications, or users) while costs follow a more or less linear function. Take for example a wireless network: if you have only 2 mobile devices, they’re only able to communicate with each other. However, if you have billions of connected devices and applications, opportunities rise dramatically.


Figure 3: Metcalfe’s law

So, Metcalfe’s law is really about network growth, value creation, and customer acquisition rather than about technological innovation.

A good example of the above is the revenue growth of Facebook, which has been following an almost perfect Metcalfe trajectory.


Figure 4: Facebook growth is following Metcalfe’s law

The combination of Moore’s and Metcalfe’s laws explains the evolution of communication networks and services, as well as the rise of the Internet of Things. The current IoT growth is enabled by hardware miniaturization, decreasing sensor costs, and ubiquitous wireless access capabilities that are empowering an explosive number of smart devices and applications (with a predicted CAGR of over >50% for the next 5 years.)

Unfortunately, general availability of state-of-the-art technology is not always a recipe for success. Some of the designated IoT “killer” devices and apps, mainly in the consumer domain with smart watches and connected thermostats as notorious examples, are still struggling for broad user adoption.


Figure 5: Worldwide wearable device shipments by the Top 5 vendors in 2015 (in million units)

Crossing the chasm

To explain the rather slow take-up of connected devices, we have to take a look at the technology adoption lifecycle, and more specifically at the “chasm theory” that was developed by management consultant Geoffrey Moore, based upon Everett Rogers’ Diffusion of Innovations curve.

In his book “Crossing the Chasm: Marketing and Selling High-Tech Products to Mainstream Customers,” Moore writes about the gap, a.k.a. the chasm, that product marketers have to bridge for the take up of new technology by early enthusiasts and mass market adoption.


Figure 6: technology adoption curve with the chasm

According to the author, early adopters are technology enthusiasts looking for a radical shift, while the early majority wants a productivity improvement.

“What the early adopter is buying, is some kind of change agent. By being the first to implement this change in their industry, the early adopters expect to get a jump on the competition, whether from lower product costs, faster time to market, more complete customer service, or some other comparable business advantage. They expect a radical discontinuity between the old ways and the new, and they are prepared to champion this cause against entrenched resistance. Being the first, they also are prepared to bear with the inevitable bugs and glitches that accompany any innovation just coming to market.

By contrast, the early majority want to buy a productivity improvement for existing operations. They are looking to minimize the discontinuity with the old ways. They want evolution, not revolution. They want technology to enhance, not overthrow, the established ways of doing business. And above all, they do not want to debug somebody else’s product. By the time they adopt it, they want it to work properly and to integrate appropriately with their existing technology base.”

But there’s more behind the chasm than consumers being slow (or conservative) in adopting new technologies, products, and services. As Clayton Christensen suggests in “The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail”, successful companies put too much emphasis on customers’ current needs, and fail to adopt new technology or business models that will meet their customers’ unstated or future needs. In other words, they need to deliver disruptive products and services. For example, the iPhone was a disruptive product, but the Apple Watch wasn’t.

A shift is happening within the chasm

By combining the three preceding charts and admittedly visually cheating with axes, scales, and representations, we can come to the conclusion that the chasm is actually the point where the shift from a technology driven model to a value and customer experience driven business needs to take place. If this doesn’t happen, any new product or technology introduction is doomed to fail.


Figure 7: the tipping point of technology and value

In a more recent article, “The Nature of the Firm – 75 Years Later,” Geoffey Moore writes:

“Smartphones and tablets are reengineering whole swaths of the consumer economy, from information access (Google) to communication (Facebook and Twitter) to media and entertainment (YouTube) to transportation (Uber) to hospitality (Airbnb) to dining (OpenTable and Yelp), and beyond.” […] “At the same time, the big data analytics and cloud computing that enabled consumer IT to scale are now also being coopted by enterprises to help them scale their reach and increase their efficiency and effectiveness. […]

“Not surprisingly, transaction costs decrease—dramatically! All the overhead, all the delays, all the errors, all the confusion created by complex systems and well-intentioned but imperfectly informed human beings—all that sludge is being flushed from the system,” […]
“as transaction costs decrease, the value of services relative to products increases. That is because one of the key selling points of a product is that it eliminates future transaction costs once it has been purchased.”

Which lead us to the zero marginal cost model…

Towards zero marginal cost and infinite value

In “The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism,” American social-economic theorist and activist Jeremy Rifkin describes how new technologies, such as 3D printing, green energy, and the Internet of Things, are quickly moving us to an era of nearly free goods and services (and, according to the author, the eclipse of capitalism.)

“Billions of sensors are being attached to natural resources, production lines, the electricity grid, logistics networks, recycling flows, and implanted in homes, offices, stores, vehicles, and even human beings, feeding Big Data into an IoT global neural network. Prosumers can connect to the network and use Big Data, analytics, and algorithms to accelerate efficiency, dramatically increase productivity, and lower the marginal cost of producing and sharing a wide range of products and services to near zero, just like they now do with information goods.”

The programmable world will have an immense impact on many industries. Take for example the logistics and transport sector: access to real-time data, like the location of goods, current weather conditions, traffic flows, and up-to-the-moment information on warehouse inventories and capacities, will dramatically reduce the marginal cost of production, storage and delivery of goods.

At the same time, the convenience brought by IoT technologies will have a tremendous added value for a lot of companies and consumers, since it will boost productivity, increase efficiency, and improve the end-customer experience. As such, it would be a fair statement to make when saying that the long-term value of IoT is nearly infinite…

In the next section, we will, based on real market data, make an estimate of this long term value.

Calculating the long term value of IoT

Let’s start the quantitative analysis phase of this paper by looking at Moore’s law. Moore’s law is typically calculated as the number of components per integrated function in an electronic circuit. The theory states that the number of components per given functionality in an integrated circuit doubles every two years. This translates in a number of ways – circuits can get faster at a fixed size, smaller at a fixed speed, lower cost at a fixed speed and size, or infinite combinations of any of the three factors. The end product is optimized based on its intended use.

We begin to build our Moore’s law curve by starting in 1971 with the Intel 4004, which had 2,300 transistors per CPU. Then we extrapolate that forward to 2021. On the chart that follows, we show the density and cost evolution between 2013-2020 in billions of components per function.


Figure 8: Moore’s law for the IoT

Between 2015-2021, the performance of electronic circuits will increase by 8 times. This means that the cost of a fixed function will decrease by the same amount (8 times). Sensors will become much less expensive and smaller, and can be widely deployed at low cost. The data collected by these sensors can be transmitted more cost effectively, and analytics computing power applied more quickly and cost effectively.

Next, let’s look at quantifying Metcalfe’s law. The objective is to be able to compare Internet users and their implied value to IoT devices to be able to make a relative comparison of potential. We begin with the number of Internet users from 2000-2016. We also obtain a projection of Internet users for 2020. In addition, we plot the number of connected “things” and their respective squared values. In this example, conservative numbers for connected “things” are used, reaching 21 billion by 2020. Some projections go as high as 100 billion “things”. The latter value exaggerates the value of IoT beyond what is used in this example. We plot these values on the following chart, and also plot their “value” based on Metcalfe’s theory with 2013 as the baseline.


Figure 9: Metcalfe’s law for IoT

Based upon this extrapolation, we conclude that by 2020 IoT will have a value, based on the number of connections, that is 7.5 times that of the Internet in 2020 and about 36 times the value of today’s Internet, assuming the node values are equal.

As the number of devices connected through IoT increases due to Moore’s law, and subsequently drives down the cost of the technology required, the value of IoT accelerates. With costs dropping and value increasing exponentially, new business models become viable and sustainable. This is when IoT moves from being a technical “wow” to an actual business, and in other words, it “crosses the chasm”. And acceleration continues because now it can be used to reduce transaction costs and push inefficiencies out of the system.

A standards-based horizontal IoT platform fuels exponential growth

Many of the early IoT applications came to market following a vertical model, in which each solution was implemented on separate infrastructure, which needed a new and/or tailored IT development effort, and used a separate management and execution platform. Such a siloed approach not only has an impact on development, deployment, and operating costs, but it also constrains the opportunity to realize meaningful economies of scale, develop solutions that can apply to more than one vertical market, and benefit from application interworking and data sharing.

Although at first sight IoT is all about vertical solutions that beg for a vertical model, there is a clear business rationale for an “any network, any device, any application” platform with a common set of service capabilities, standardized interfaces, and open APIs enabling faster growth and innovation, as a broad variety of devices and applications can use common infrastructure, and share platform functions and data.

Actually, a horizontal IoT platform will boost the network’s value as defined by Metcalfe’s law. If we take the example configuration below. When N different vertical applications (A) are deployed on N different “stovepipe” platforms (P) with M connected devices (D)each, then the discrete y-value of Metcalfe’s curve is equal to N*(M+1)².


Figure 10: Metcalfe’s law for N vertical applications

But, if we assume a common, horizontal platform that connects all N applications to all N*M devices, then the network value explodes exponentially to (N*(M+1))².


Figure 11: Metcalfe’s law for N applications on a horizontal platform

Furthermore, a horizontal platform, with open interfaces and standardized protocols, will allow IoT service providers to reduce investments, lower operational expenses, scale faster, speed up time-to-market, facilitate partnerships, and deliver a better customer experience. While device vendors and application developers can concentrate on their real differentiators, rather than on re-implementing the common functions that will be provided by the platform.


Moore’s and Metcalfe’s laws drive the chasm crossing and in doing so, enable increasing value while catalyzing the continuing drive toward zero marginal cost and into a dynamic world that continually adjusts and optimizes seemingly on its own. This world comes about when sensing, connecting, and computing costs no longer prohibit ubiquitous deployment and when horizontal IoT infrastructure, like Nokia’s IMPACT platform, help maximize synergies between an ever growing number of devices, data records and applications.

By understanding in qualitative and quantitative terms how the confluence of all of these factors drives IoT, it’s quite easy to see why many predict the value of the Internet of Things to be in the trillions.

(this article was originally published in 2 parts in Nokia Techzine on 27 June 2016)


Source (business2community)

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