Edge computing and Industrial IoT expert Jim White, CTO at IOTech, provides his top five predictions and honourable mentions for the coming year, including forecasts related to security, hyperscalers, and AI/ML

The Top 5 Edge Computing/Industrial IoT Predictions for 2023

1.            Edge play time is over

Companies that are wanting to put edge/IoT solutions in place are making things clear to providers: research and play time is over. 

These companies are done “trying” things. Edge solutions have to work “now,” they have to work at scale, and they have to work such that IT and OT teams can use them effectively.

Companies are growing impatient with solution providers that are not able to provide solutions that are already working at scale and immediately demonstratable. Edge elements must be fully integrated into their choice of technology (hardware, sensors, devices, network, cloud providers, data visualization, analytics, security, management, etc.). Companies want edge solutions that are easily installed and even easier to own and operate. 

2.            OT Edge Security becomes a thing

Threats at the edge are becoming more publicized and known. Companies are reading about various attacks on the edge and they are becoming educated on what they want for solutions. Requirements are becoming clearer and more specific. 

Companies are no longer under the illusion that closed loop networks are truly closed, that obfuscation is good enough protection because “this stuff is complicated,” or that “no one would bother to want to get access to this type of data.”

Organizations want to know how to protect all parts of the edge solution, from sensor to cloud.  They also want to know how to detect when something seamy or unexpected seems to be going on. Progress is being made with edge/IoT security capability, but much of that is related to protecting cloud native environments and doesn’t integrate well at the edge. Edge/IoT and security industries are starting to recognize this.

3.            Reinvention and disruption of hyperscalers

Cloud providers and the hyperscalers have tried to lure all that precious edge data into the cloud where AI/ML and other analytics were to operate on it. The challenge is that the vast transfer, storage, and compute charges associated with moving all that edge data to the cloud is significantly expensive. Trying to sift through all that data for nuggets of commercial value doesn’t always show an ROI – at least not yet.  

Companies are beginning to wake up to this reality. 

Hyperscalers know how to do scale. They just need to do edge at scale and in a way that adds value and lowers cost. They can and will figure this out, but they are going to require help from organizations, people and projects that know the edge. Watch for an increase in new product announcements, new partnerships and acquisitions as the hyperscalers finally take on “edge native.”

4.            Not everything requires AI/ML

AI/ML is revolutionizing numerous industries and spaces. But as with any supposed magical balm, it can be overapplied. There is a lot of edge processing going on – some of it might even require some sophisticated calculations and algorithms – but not all of it needs costly ML models and AI engines. 

Simple rules engines and scripting engines can provide a lot of value at the edge – saving operational costs, improving safety and even generating new revenue. Edge solutions don’t always require advanced/complex skill sets to produce, nor do they require all sorts of compute power to operate.

There is still a lot of low hanging fruit (aka money to be found) by measuring a few edge values and automatically actuating when things get out of range.  Edge solution providers that help keep it simple and harvest that fruit, might become the new darlings of investors and companies looking to improve their company bottom lines.

5.            Kubernetes still not the full answer, but…

Everyone’s edge is different, so Kubernetes can be used to deploy, orchestrate and manage containerised workloads at some edges.  But Kubernetes does not solve all the issues around management at the edge and it struggles in resource constrained environments or environments that aren’t going to support containerized workloads. 

There have been and continue to be more CNCF efforts to extend cloud native – call them Kubernetes light – to the edge.  Many of these have been attempts focused on shrinking Kubernetes at the cost of functionality.  microK8s, KubeEdge, K3s are all options that have been traversing this path. 

There is growing recognition on the part of the CNCF community that Kubernetes-light isn’t enough. Therefore, 2023 will experience an emergence of new approaches and architectures to help address edge management.

Honorable Mention Predictions

A.            Consolidation

There are numerous edge and Industrial IoT platforms, software, tools, etc. (proprietary and open source). These have emerged over the past 5-10 years of the hype cycle associated to edge and IoT computing. The industry has reached a point (through the trough of disillusionment and onto the plain of productivity) where consolidation is inevitable. Companies want to accelerate their edge/IoT solutions.  Over the past few years, companies were buying AI/ML companies to gain control of the IP and the people in that space. They will do the same to consolidate more of their holding on the edge solution space.

B.            Use Case Demand Changes

Within the industrials sectors, suppliers of solutions in the edge/IoT space have been addressing use cases for several years. Now, other verticals are starting to become important consumers of edge/IoT solutions. Climate change, energy shortages, health and environment concerns, people / staff shortages – all areas of need stemming from immediate global economic and geopolitical circumstances – are driving more use cases.