The view of the industrial Internet of Things (IoT) as billions of sensors connected into intelligent systems distracts from its true role: digital transformation. The gargantuan task and investment of making all these connections, reliably and securely, is pointless unless there is a solid business reason — and there are two very good ones.

2 reasons manufacturers invest in IoT

The first reason big companies invest in IoT is they are worried about being digitally disrupted by a Tesla, Uber, or the next AirBnb in their industries — disrupt or be disrupted.

The second reason: a company’s slowing growth and the need for increased productivity. BCG’s Olivier Scalabre explained that driver during a 2016 TED Talk.

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Industrial Internet of Things (IoT) does not have the explosive growth of the consumer internet. It’s a nitty-gritty, complicated, and sometimes downright boring business. It is too complex and diverse for explosive growth like the narrowly defined, one-size-fits-all iPhone. That said, the promise of rich returns on investment from new business models that are possible with IoT is very compelling.

The only way to cut through the venture-driven IoT hype cycle is with conversations with builders and implementers at companies investing in IoT, such as Cisco’s co-innovation center chief, Maciej Kranz.

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The point of my blog post, "What the IoT industry can learn from Apple’s revival of the Mac," was to use Apple’s pivot to the Intel platform and compliance with the Open Group’s Unix standards as an example of the importance of a large vibrant ecosystem (created by Intel and Microsoft) is for the IoT industry to follow.

Without the PC ecosystem, the Mac would be an obsolete machine exhibited in the Computer History Museum. The PC ecosystem eliminated much redundant development, letting component and product companies focus on their products’ differentiation. The IoT industry would benefit if it had a robust ecosystem the eliminated redundant development. 

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The Internet of Thing’s dirty little secret is the cost of deployment. For example, adding a low-cost motion sensor and radio to a traffic light to count passing vehicles before it leaves the factory is easy and inexpensive. The incremental cost of deployment is near zero, especially if low-power wide-area network (LPWAN) coverage or other low-cost communications coverage is available. But retrofitting the traffic light with sensors and radios will cost the municipality a public works truck roll, a crew, an electrician and a police traffic detail. Retrofits are expensive.

Retrofitting the world for IoT is a data science and a sensor engineering task to study the IoT problem and find the simplest way to acquire the data. The question for the data scientist is what minimum data resolution will provide the desired result: How many sensors per unit of area and how many readings per time interval are necessary to solve the problem? The engineer must trade off sensor costs, radio costs and network availability, and power consumption versus available power sources.

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Sensor Fusion for Public Space Utilization Monitoring in a Smart City (pdf) is simply the best read for IoT product designers, developers and implementers. It steps through designing a system to measure space utilization in a city — the trade-offs made in sensor selection and calibration, power source selection, network design, data cleaning and normalization, and data processing. The methodology can be generalized for designing any IoT network. The paper is nothing less than a perfect case study about how to build an IoT network.

The most interesting aspects of the paper by Billy Pik Lik Lau, Nipun Wijerathne, and Chau Yuen of the Singapore University of Technology and Design and Benny Kai Kiat Ng of Curtin University is how they matched the sensors to acquire the data at the right resolution to estimate space utilization and built a test bed, minimizing a wide range of implementation issues. To measure space utilization, meaning how populated a space is over multiple time intervals, they chose sound and motion sensors and the fusion of the two. The methodology applied in this paper could be adapted to other sensor types.

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Update: In response to a post on Daring Fireball saying I had incorrectly stated things about the Mac, I have written a reply — Rebuttal to Daring Fireball: FreeBSD, Intel and Microsoft did save the Mac — which includes a correction and clarifications.

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This post is an excerpt from a talk I gave at the Reality Virtually Hackathon that makes a similar comparison between virtual reality and augmented reality and the Mac. It holds true for the Internet of Things (IoT) and every emerging technology.

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Nvidia and server makers Dell EMC, HPE, IBM and Supermicro announced enterprise servers featuring Nvidia’s Tesla V100 GPU. The question is, can servers designed for machine learning stem the erosion of enterprise server purchases as companies shift to PaaS, IaaS, and cloud services? The recent introduction of hardened industrial servers for IoT may indicate that server makers are looking for growth in vertical markets.

There are very compelling reasons for moving enterprise workloads to Amazon, Google, IBM and other hosted infrastructures. The scalability of on-demand resources, operating efficiency at cloud-scale and security are just three of many reasons. For instance, Google has 90 engineers working on just security where most enterprises are understaffed.

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Three characteristics of the Internet of Things (IoT) differentiate it from industrial automation.

1. IoT devices are inexpensive.
2. IoT devices can be ubiquitously connected everyplace and anyplace.
3. IoT devices have inexpensive or zero-cost deployment.

It explains why we see so few IoT networks and why most of the industrial IoT forecasts are measurements of industrial automation that we have had for decades.

The first one, with the exception of the issue of strong security, is easy. The second two, though, in New Jersey parlance — says easy does hard.

Ubiquitous connectivity is talked about, and there is a glimmer of hope presented by Low-Power Wide-Area Networks (LPWAN) such as Senet that focus on both low-cost technology and a business model for entrepreneurial partners to deploy networks. But waiting for carriers to perfect and deploy 5G networks to build IoT solutions will delay innovators and prevent early adopters from building proof-of-concept and prototype networks essential for the iterative learning of technical methods, business cases and making financial projections of the benefits of IoT.

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A machine learning project to build an autonomous sailplane that remains aloft on thermal currents is impressive enough. But the work conducted by Microsoft researchers Andrey Kolobov and Iain Guilliard will also improve the decision making and trustworthiness of IoT devices, personal assistants and autonomous cars.

The constraints limiting the computational resource of weight and space imposed by the airframe of the sailplane adds relevance to the many new developments in ubiquitous computing. The autonomous sailplane is controlled by a 160MHz Arm Cortex M4 with 256KB of RAM and 60KB of flash running on batteries that monitor the sensors, run the autopilot and control the servo motors, to which the researchers have added a machine learning model that continuously learns how to autonomously ride the thermal currents.

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If Akamai, Cisco and Google’s post-platform security and privacy machine learning security systems protecting the web and mobile platforms are indicative of the future, IoT device makers will only be part of a larger security ecosystem. That’s because they will not have the data to train the AI machine learning models.  

As a result, IoT post-platform security and privacy will become a layer on top of IoT device security. These five factors are why that will happen.

1. Product developers underestimated IoT security

In their race to market, product developers building for new platforms will underestimate the security and privacy features that should be built into their products. In some cases, this will be an act of commission, but most will be an act of omission because it is difficult to anticipate the vulnerabilities until the products reach the market at scale. Windows and mobile devices experienced something similar. They have been hardened, but earlier in their evolution they were an easy target for cyber criminals.

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Much work still must be done before the industrial and municipal Internet of Things (IoT) becomes widely adopted outside of the circle of innovators. One field, privacy, well understood by the public and private sector in the context of the cloud, PCs and mobile, is in the early stage of adaptation for the IoT.

The sheer volume of data that will be collected and the new more granular architecture of the IoT present new privacy concerns that need to be resolved on an equal scale as the platform’s forecasted growth.

A demonstration of this new aspect of privacy and compliance is the Privacy Guidelines for Internet of Things: Cheat Sheet, Technical Report (pdf) by Charith Perera, researcher at the Newcastle University in the U.K. The nine-page report details 30 points about implementing strong privacy protections. This report is summarized below.

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Weather on Mount Washington in New Hampshire can be biblical. On one occasion, I started an early-morning April ascent with fresh snow at the base. Mid-day we stripped to our base layer of clothing when the bright sun warmed the temperature to 60°F. At the peak elevation of 6,288 feet, dark clouds closed in, the temperature dropped and the wind picked up. We ran for cover from lightning that had a very short distance to travel between the low clouds and the high peak to travel.

After hiking the mountain a half-dozen times, in all seasons and all conditions, it’s interesting to learn how the Mount Washington Weather Observatory on the peak uses IoT to update weather conditions on this frequent hiker destination.

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The hazard of unsophisticated and poorly secured Internet of Things (IoT) devices came to the front last year with the Mirai DDoS attack that involved nearly a million bots. Many of these devices remain a threat.

Researchers have posed an original solution to the problem: Use the vulnerability of these devices to inject a white worm that secures the devices. It is an epidemiological approach that creates immunity with a vaccine by exposing the immune system to a weakened form of the disease.

+ Also on Network World: How to improve IoT security +

These devices are still a threat because some cannot be fixed because they have hard-coded back doors. Other insecure devices have software or firmware vulnerabilities that cannot be fixed because product designers did not include a software updates mechanism.

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The first rule of building a secure and feature-rich ecosystem is software management — push and pull software updates and software discovery through an app store mechanism from a trusted source.

In the go-to-market IoT race, though, that often doesn’t happen. Many Internet of Things (IoT) product developers have ignored the traumatic early history of Microsoft Windows, Android and web platforms, and expoits of IoT devices — because software updates have not been designed in — are regularly reported.

+ Also on Network World: How to improve IoT security +

Those earlier platforms have been hardened, updates have been automated, and the app discovery and installation have been made trustworthy. IoT developers need to follow their lead. 

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The tsunami-sized trend to add intelligence with sensors and actuators and to connect devices, equipment and appliances to the internet poses safety, security and privacy risks.

Proof comes from a recent meta-study titled The Internet of Hackable Things (pdf) from researchers at the Technical University of Denmark, Denmark; Orebro University, Sweden; and Innopolis University, Russian Federation—compiled from industry and academic research reports—that finds smart devices used in healthcare and smart homes and buildings pose daunting risks.

The authors quantify the risks of Internet of Things (IoT) devices:

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Google’s former HR chief, Jonathan Rosenberg recounts in his book How Google Works a story about CEO Larry Page’s response to an MBA employee’s PowerPoint product plan. Page told the MBA, “Go sit with the engineers.”

Page’s point: An effective new product plan can be created only if the engineering details are understood.

Along this line of thinking, I spoke with Joe Biron, Internet of Things CTO at PTC, because the company has staked its future on IoT, and Biron has a decade of IoT engineering experience. PTC’s mature businesses, mechanical design, and product lifecycle management are closely related to the company’s industrial IoT business, where the company is focused for growth.

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Without simulation, complex systems would fail. Satellites would not reach an accurate orbit, semiconductor circuits would not function, and bridges would not carry the load. Businesses and governments would not invest in these projects without robust simulation software. And without a simulation proving value and functionality, IoT networks of hundreds of thousands or millions of inexpensive devices adding up to large capital investments will not be built.

Researchers from the University of Bologna published an analysis of IoT simulation and a smart cities vehicular transportation system case study (pdf). They recommend a networked simulation of orchestrated simulators that model specific IoT features that fit the diversity of IoT devices and use cases.

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Outside of the consumer market, real examples of the Internet of Things (IoT) often disappoint because in the end, they are limited by one or more of the IoT’s constraints: tens of billions of devices, cheap to acquire, cheap to deploy, security and ubiquitous connections.

I spoke with Dave Kjendal, Senet’s vice president of engineering and CTO, because he has built products and networks that meet these constraints. It was insightful because Senet has produced products using the entire IoT stack. 

Senet’s evolution began in 2009 with low-cost fuel oil tank sensors communicating over the unlicensed airwaves to optimize delivery routes. The company now operates a general purpose LoRaWAN IoT network that covers one-fortieth of the United States. LoRaWAN is an implementation of low-power, wide-area networks designed to transmit small messages at a frequency of about one an hour. It serves about 55 percent of IoT WAN connectivity. It is a different technical approach than what the mobile carriers promise with 3GPP, which is yet to be standardized.

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Earlier this week, the Bluetooth Special Interest Group (SIG) announced the Bluetooth Low Energy (BLE) standards have been extended to include mesh network features. It is clear that the Internet of Things (IoT) is the intended market. The SIG says:

Bluetooth Mesh is “ideally suited for building automation, sensor networks and other IoT solutions where tens, hundreds, or thousands of devices need to reliably and securely communicate with one another.”

Mesh networks are not new. It is a network topology in which each node relays data for the network. All mesh nodes cooperate in the distribution of data in the network. The IoT-purpose-built Zigbee—a low-power, low-bandwidth ad hoc network—is a mesh network. Dating to 2002, Aruba Networks was founded to build Wi-Fi mesh networks. In 2014, student protesters in Hong Kong used mobile app FireChat to turn the crowd’s smartphones into a Wi-Fi and Bluetooth mesh network so authorities could not interrupt protester’s coordinating conversations by blocking 3G and 4G network access. Bluetooth Mesh has some very desirable features:

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After I read Brian Bailey’s IoT semiconductor design article, IoT Myth Busting, I thought of Prince’s song 1999, in particular, the line:

“So tonight I'm gonna party like it's nineteen ninety-nine.” 

Without a lot of irrational exuberance, we won’t see IoT edge and fog networks soon

Most IoT applications are prototypes and proof of concepts (PoC) designed to justify enterprise budget increases and follow-on venture investment rounds. Unless we return to and party like it is 1999 when telecoms over-invested in capacity ahead of demand, the telecom carriers are not going to build the new fog and edge networks that IoT needs to grow ahead of demand. At this stage, we would have to see a return of the irrational exuberance, a term coined by Federal Reserve Chairman Alan Greenspan, used to describe the over investment and over valuation during the dot-com bubble.

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The IoT in the commercial sector might better be called the Internet of Prototypes, the IoP.

Few of the components for building the ubiquitous IoT that the future holds are available today. The best way to envision the future is by prototyping. Prototypes of mission-critical or high-ROI applications will tease money out of research budgets to build them. All the prototypes will lead to a greater understanding, and when the cost of the problem matches the development investment  the prototypes will become products. With cost reduction and standardization, products could become generalized extensible platforms.

+ Also on Network World: How industrial IoT is making steel production smarter +

MIT built a fitting prototype that could, with further development, scale into a platform. A multidisciplinary team from the MIT Design Lab led by MIT Media Lab researcher Guillermo Bernal won best research paper at the Petra Conference last month for the team’s work applying IoT and wearables to industrial safety. The sophisticated and purpose-built prototype at the center of the research makes the paper “Safety++. Designing IoT and Wearable Systems for Industrial Safety through a User-Centered Design Approach” extremely tangible and predictive about how the IoT will unfold.

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I’ve been reading and writing daily about the Internet of Things (IoT) for about a month, and I have not found ground truth. I’m not new to the field, but now focused on IoT I am trying to find a relative measure of the importance of IoT developments.

One of the definitions of ground truth in the Oxford Dictionary is:

“Information obtained by direct observation of a real system, as opposed to a model or simulation; a set of data that is considered to be accurate and reliable, and is used to calibrate a model, algorithm, procedure, etc.”

Calibrating the development of the IoT with ground truths that match the 20 billion to 50 billion forecasted devices requires a departure into the leap-of-faith lane. This is not pessimism, but a lack of published information that probably does not exist outside of research papers.   

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The mobile internet was a simpler infrastructure to design than the one that will be needed for the smart city, smart grid, smart health and smart transportation. Smartphones are homogeneous with relatively powerful processors and batteries driving transmission and reception. Designed to bring the internet to smartphones, 3G and 4G networks could be simpler. But IoT devices will span a range of heteroneous designs.

The range of heterogeneity of the IoT is defined today by autonomous vehicles, which have thousands of sensors powered by high-capacity batteries that frequently communicate at high speed and low latency to simple sensors. Those sensors are powered by ambient power sources, sending a few infrequent bytes to communicate state (on/off, temperature, vibration amplitude and phase, etc.).

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I met with a team of Microsoft AI researchers recently to discuss original adaptations of Resnet 50, a version of the convolutional network Microsoft used to win the Imagenet 2015 image recognition competition. The discussion about the scientists work caused me to reconsider the inextricable link between IoT and machine learning.

Control loops are a fundamental principal of the internet of things (IoT.) If then, then that (ITTT) has a long history in conditionally controlling things dating to the invention of the electric relay in the 1830s. Over time, single relays were combined into state machines, and later, relays became transistors. During the glamorous growth of computers in IT, consumer and mobile sectors, less glamorous ITTT computers have been applied to many use cases such as controlling machines in factories and performing lab experiments.

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Fog computing and fog networking could fill the latency and range gap in the internet of things (IoT.) For the last couple of years, researchers have been reporting on developments in fog’s role in completing IoT’s ubiquitous connectivity. It is similar to cloud computing architectures but it brings the cloud to the edge to meet the different demands of IoT.

The underlying concept is the cloud for some real-time IoT services could be too slow because the quality of service (QoS) specifications for the IoT application exceeds the cloud’s QoS. The solution is to move the cloud out into the network.

Low latency and QoS is important in IoT use cases like self-driving vehicles and controlling robots and other control applications that require minimum latency to synchronize, supervise, control and initiate machine actions. Range is important when connecting devices over long distances where hubs and gateways are not locally available.

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The devil is in the details – best describes IoT. Computing will vanish says Walt Mossberg. And the public and data scientists gush with optimism at the thought of 20 billion to 50 billion connected IoT devices emitting an endless stream of data.

Getting the billions of devices connected will be no small task. The thought of all the IoT applications of all that data is nothing less than seductive. Cities will self-regulate motor vehicle and public transportation saving commuters hours of travel time. Sensors will alert us and doctors when mom is not keeping to her pharmaceutical regimen.

And, all these IoT devices will give context to people’s daily lives. Lights dimming or illuminating to match our mood or actions, refrigerators will warn that we have either eaten too much or not exercised enough before opening the door. Our designer glasses with integrated Microsoft Hololens technology will project the most salient context of the moment at that location.

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Both Gartner and Tech Insider agree, the Internet of Things (IoT) will be a major tech category, predicting  20.4 billion units and 23.9 billion devices, respectively, by 2020.

They disagree, however, about where those devices will go. Gartner says two-thirds of the devices will land in consumer applications. Tech Insider says over three-fourths will land in government and business applications.

+ Also on Network World: 5 things to think about for industrial IoT readiness +

Why is there such a big difference in forecasts? Which forecaster’s crystal ball is less occluded? Gartner’s forecasts depend on more stable, smaller IoT consumer islands, while Tech Insider’s forecasts depend on large clouds often built on evolving technologies.

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Researchers from the University of Michigan and Stony Brook University published a paper explaining a novel approach to IoT security challenges (pdf). The researchers pose the question:

 “What are the new intellectual challenges in the science of security when we talk about the Internet of Things, and what problems can we solve using currently known security techniques?”

This research approach is very accessible because it uses existing categories and concepts by comparing security methods developed for smartphones, PCs and the cloud to identify the gaps and challenges to IoT security. The IoT stack is defined with the familiar layers:

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As artificial intelligence (AI) increases in importance in new technology and applications, several people have risen to the top of the field—achieving expert-level status and providing insight into breakthroughs, new applications and ideas about the technology.

The people on this list are an interesting collage with very different backgrounds. Some are traditional AI Ph.D. scholars who have slogged through research long before AI’s recent resurgence. Others are cross-over experts from one advanced science who saw the benefit of AI in their research and became experts in a second field. 

+ Also on Network World: What AI can and cannot do today +

The short summaries of each persons’ biographical information add depth to the tweets. The biographical information was extracted from public sources, such as Twitter, LinkedIn, Wikipedia, university websites, Crunchbase and business websites. Given the suspicions about the authenticity of news stories, the biographical information about each person adds a level of diligence to choose if the reader finds personal value in following one of them.

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Last October, when Google Home was announced, Google CEO Sundar Pichai christened AI as the next platform. Yesterday, AI became a Google product that could become as transformative and as large and potentially pervasive as Google Search.

Google’s grand bargain with its users will not change: indispensable free apps in return for users’ data. Easier to use conversational interfaces such as Google Home and Google Assistant built with AI could be the next free indispensable Google app purchased with the users’ information as the currency. It is a virtuous cycle. User interaction with indispensable apps like Google Search, Translate and Assistant that use AI, creates more data to create new indispensable AI systems. Pichai confirmed this during the Google I/O keynote when he said:

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