The Trend of the Twenties: Edge-Native and the roadmap to achieve it

The Internet of Things (IoT) marketplace continues to evolve. There are currently ~ 23 billion connected devices operating worldwide. Most objects and machines are now IOT enabled. And yet, we are still awaiting the deployment of IOT enabled applications and ultimately see hyperconnectivity, the proper interaction between system, data, and humans, become a reality. True in our daily lives, hyperconnectivity already exists. We can use our cell phones to control the TV, the air conditioning, or the heating systems minimally but this is barely scratching the surface. Why is that? What have been the limiting factors?

Growth Inhibitors

By definition, hyperconnectivity is the ability for systems, devices, and humans, and there are mainly three growth inhibitors, the cost of those solutions, dumb devices, and a low latency application deployment environment.  

From household appliances to health tracking devices to smart manufacturing equipment, many use cases have not found an adequate ROI due to the costs associated with implementing those types of solutions. According to Topio Research, most applications do have savings or increased revenue, but the cost of implementing and maintaining those solutions has been extraordinarily high. Adding extraneous hardware, high bandwidth cost, and managing other applications at the Edge are only examples of the costs for those types of applications. There is also lingering concern about the security of data collected and processed in the cloud.

The Solution: The Shared Edge Computing Infrastructure

We often see people talking about the killer app for IOT as if one application could justify the cost for the entire infrastructure. If we look back twenty years ago with the cloud and the ten years with the mobile application platform, there was no single use-case that drove the adoption of those platforms. In the case of the cloud, one vendor, Amazon, built the platform, and software developers started to develop applications. If each application was not enough to justify the ROI of the cloud, the availability of the cloud infrastructure and its open ecosystem enabled the creation of 10,000s of applications and a  $ 450 billion industry scheduled to go US$ 1 trillion in 2026. This same phenomenon happened with the mobile phone industry, anyone application didn’t justify the mobile phone software infrastructure, but the shared infrastructure did. Apple and Google built a software infrastructure, and the ecosystem built on top of it generated more than US$ 80 Billion in 2020 and should generate more than US$ 320 Billion in 2026.

We are seeing the emergence of the Edge Computing Platform in many industries. This platform provides a low-cost environment to deploy and maintain applications at the edge as if they were hosted in the cloud. Such a platform represents a significant rationalization of the existing infrastructure at the customer’s edge. In addition, applications are maintained in the cloud and are not seen as another problem to be managed locally.

“We all understand that the amount of data created continues to grow, fed by trends like IoT and the increasing range of applications with which users interact,” said Gavin Whitechurch, Principal Analyst, Edge Computing at Topio Networks. “To drive more value from that data, there is huge growth in the deployment of analytics and AI.  But for various reasons, including cost and privacy, it doesn’t make sense to shift that data through to the center of the network, where the cloud resides, to be processed –  so there is an explosion of processing data at the edge. A decade ago, the cloud-native trend emerged to create apps designed and built to exploit the scale, elasticity, resiliency, and flexibility of the cloud. But the trend of the twenties will be edge native – creating apps designed and built to take advantage of efficiency, cost-effectiveness, privacy and low latency of edge computing.”

As we are building landscapes for the different industries, store of the future, manufacturing and robotics, and agtech, it is relatively straightforward that we will 100s of edge native applications running on Edge Computing platforms leveraging all the data created locally. This is happening here and now.

Moving forward, two other trends will significantly increase the development of edge native applications. The first is making the device and objects smart and the second is the ultimate end goal of hyper-convergence.

Smart Devices: 3 to 5 years away

If we look back at the emergence of the mobile industry, it took the emergence of smartphones to see an explosion of applications and growth. We expect the IOT to follow the same trend.

Making a device smart starts with Endpoint AI, which means AI embedded and trained right at the device or machine. Machines are not computers, and they usually run on embedded systems composed of microcontrollers and components. According to IC Insights, there are about 250 billion microcontrollers in the world today, and 38.2 billion will be sold annually by 2023.

This increase in the growth of microcontrollers sold is primarily due to the need to connect IoT devices to the Internet and start making sense of and then acting on the data that those devices generate. Many applications will be created to leverage the available data, and an increase in performance and low power usage will be the driver of many more applications to come. 

And this future is already in motion. Semiconductor companies invest billions of dollars in developing more energy-efficient hardware and algorithms. Software companies like Google are also developing frameworks, such as Tensorflow lite, specifically for this purpose. Corporate and venture capital investment has now reached more than US$ 54.8 Billion, and start-ups like Kneron and ETA compute are making steady progress. The microcontroller companies are also working on these technologies, and some of them are already using ARM Cortex M processers and can also leverage ARM’s Ethos-U55 AI. TinyML, the industry association promoting Tensorflow lite, believes that advanced machine learning should hit the market in two to three years, and killer apps seem to be 3 to 5 years away. 

Hyperconnectivity: 6G – 5 years and beyond: By that time, as the number of connected devices and data traffic increase exponentially every day, future data-intensive applications like AR/VR, holographic communications, V2X, autonomous driving, high-precision manufacturing, and ultra-massive machine-type communications will emerge. These new applications will require high-throughput, ultra-reliable transmission, extremely low latency, and high energy efficiency. We expect 6G to extend 5G capabilities to higher levels where millions of connected devices and applications could operate seamlessly with trust, low latency, and high bandwidth. Once devices are smart and connected and a low latency infrastructure is available where trust is enabled, human devices and systems will collaborate to perform true autonomous tasks, enabling true hyperconnectivity.