Where the IoT Market is Heading

I delivered a presentation some weeks back at an online conference for the managed-services industry. My talk was about the implications of IoT for digital transformation [1]. To prepare for the presentation, I began by looking back over the past decade of market developments, joining a sequence of past and present developments to see into the future of IoT. This exercise provided useful insights into the evolving pattern of customer needs, consequences for where the market is heading and, implications for strategy and business innovation.

Opportunities to Apply AI and ML in IoT Systems

My last article [1] introduced a framework to explain the basic elements of an IoT system with the aim of highlighting where Artificial Intelligence (AI), Machine Learning (ML) and Digital Twin (DT) components are typically added. 

The aim of this article is to explore the longer-term opportunities for AI/ML technologies and how these will shape mobile operator and technology provider business strategies. There are two developments to consider in drawing out this roadmap. One is the tighter integration of IoT and AI/ML technologies vertically along the technology stack. Think of this as a way of improving how well different components interact to improve reliability and service quality. The second concerns a new set of requirements that users and regulatory agencies will expect from AI/ML systems. As an illustrative example, consider an AI application that issues an alarm that a machine is about to fail with some probabilistic context such as “greater than 75% chance of failure in the next month”. Is it enough to stop a production line based on this read out? In practice, there is likely to be a higher-level requirement that determines the trustworthiness of this alarm based on its performance over time. Like the boy who cried ‘wolf’, does a sequence of alarms point to a deteriorating piece of equipment or a faulty sensor? The judgement required here involves a different set of data and potentially the involvement of other, supervisory AI/ML sub-systems.

A Framework for AI and IoT

Early in my career, I had the good fortune to work on several research and innovation projects on the topics of AI and ML. These project used mathematical techniques to model the dynamic behavior of machines and to work out if they were developing faults. This are known as ‘condition monitoring’ and ‘predictive maintenance’ procedures. These projects involved data from real machines, not simulations. They included an industrial scale diesel generator, a gas-turbine used for ship propulsion and, black-box data from military aircraft [1].

In modern terminology, these projects involved the creation of digital twins from IoT data. They began by collecting time series data around events such a change in operating speed. This is important because systems do not provide dynamically rich data under static operating conditions. Think of a lightbulb with a hairline crack in its filament. Unless you have incredible eyesight, it is impossible to tell if the lightbulb is work on not. However, if you gently tap the lightbulb, you will hear the filament vibrate. That is what reveals that the broken is bulb. In addition to the signal processing aspects, this diagnostic and testing process relies on our mental model of how filament lightbulbs work.

Regulation and Competitive Advantage

A couple of years ago, I was in conversation with a group of technologists and investors at the annual meeting of the Transportation Research Board. This gathering takes place every January in Washington DC. Think of it as the transportation industry's equivalent of Mobile World Congress. 

Our group was discussing the then emerging market for connected cars. I threw in a question about the impact of regulation on their business strategies. Regulation matters in relation to safety, liability and insurance solutions, and data management. Factors such as these matter more to commercial viability than technical innovations. The need to factor regulation into technology choices and business models was evident even then. The universal response I got from the group was that innovators needed to be given the leeway to develop the technology and novel services. Putting it explicitly, regulators needed to stay well out of the way.

The same issues are apparent as new markets develop on top of the foundations of mobile communications. One example is the sharing of consumer data derived from mobile phones [1]. Another is Facebook's difficulties in launching its Libra currency and payments initiative, ahead of regulatory buy-in.

2019 in Review: A changed IoT landscape

The turn of the year has triggered many people to reflect on what they were doing 10 years ago. With that in mind, I looked through my tracker of M2M and IoT corporate initiatives to see what has changed and what we might learn about the future. The main categories of initiative include the following: technology innovation, market entry/expansion, partnering, acquisitions/investments, distributor agreements, product/service innovation, business reorganization and outsourcing.

A more tightly knit IoT value-chain 

A snapshot of the 2009 industry covers a relatively well defined mobile-industry ecosystem. This largely centered on mobile operator initiatives, driven by leading operators and supported by GSMA efforts to develop a new market for the mobile ecosystem.