3 IoT Predictions for 2024 and Beyond

For the IoT space, 2023 was all about the device. The overarching theme was that the mobile network operators (MNOs) would finally cede control of IoT connectivity to the enterprise. 

But in 2024, it’s more about the device.

IoT industry veteran and Eseye SVP Strategy & Alliances Larry Socher once again joins Eseye CEO and IoT Leaders Podcast host Nick Earle to reflect on the accuracy of their 2023 IoT predictions and share their predictions for how 2024 will pan out:

1. Smart connectivity will play a greater role in linking intelligence between the device and the cloud, with more edge-based processing than ever before.
2. Increasing network awareness for applications will give rise to more intelligent decisions, laying the foundation for 5G.
3. Trust will play a greater role in IoT with AI increasingly impacting debates around data compliance and regulations

Learn how smart connectivity is changing industries from healthcare to finance.

Subscribe to IoT Leaders

Subscribe now

Subscribe now

Subscribe now

Whitepapers

The Rise of the Intelligent Edge: Eseye’s IoT Predictions for 2024 and Beyond

Learn more

Blogs

IoT Predictions for 2023: How Did We Do?

Learn more

Solution Papers

AnyNet SMARTconnect™

Learn more

News

Eseye Named a Visionary in the 2024 Gartner® Magic Quadrant™ for Managed IoT Connectivity Services, Worldwide for 2nd Consecutive Year 

Learn more

Transcript

Nick Earle:

Hi, this is Nick, and this is our annual IoT Leaders predictions report. And we’re going to go through why it’s even more all about the device. You’ll hear the predictions in great detail between myself and Larry Socher shortly. But if I was to sort of give one high level overview of this is that this time last year, we were saying control is moving to the enterprise. Enterprises are finally taking control of IoT. And clearly, that is the case. But when you listen to this podcast and Larry’s view as to what’s going to happen, and by the way, Larry is 26 years at Accenture in senior executive roles, has worked all over the world, very good view of the industry, and not just IoT, but the industry overall, you could almost summarize it as it’s not just that the control is moving to the enterprise. Control is moving to the application and the device. And the application is controlled by the enterprise.

But when control moves to the application, you can automate it and you can scale it. But that application will run at the edge because 80% of applications and data will be resident at the edge. So that’s the sort of high level aha of what we’re talking about here, and we go into that in a lot more detail on this podcast with myself and Larry Socher. So without further ado, let me hand over to the annual IoT predictions this time for 2024. I’m joined again this year by Larry Socher, and Larry is our SVP. He’s responsible for our strategy and our product direction. So, Larry, welcome again to the annual predictions podcast.

Larry Socher:

Great to be back, Nick.

Nick Earle:

So last year, if I recall, we made five big predictions of what was going to happen in 2023. Lots of tactical things happen, you read a lot of announcements, a lot of alliances. But we try and pull ourselves out of that and we look top down and say, “What are the big trends that we think? What do all these actions mean? What is likely to happen in the next 12 months?” So maybe we can start by going through very quickly the five predictions we made a year ago-

Larry Socher:

Sure.

Nick Earle:

… and see how we did.

Larry Socher:

Well, first of all, I’m very excited. I think we did a hell of a job last year. So I’m very proud of our predictions. So we didn’t certainly get everything right, but I think we’re pretty close. The overarching theme was about how the mobile network operator was finally going to lose control and the enterprise was going to seize control of the IoT connectivity. And the first prediction along that line was that the MNO lock would be finally broken. And there’s a combination of things that were going on in the market, the hyperscaler threat, etc.

And I think clearly, that did happen, but I don’t think we got the actual driver correct. So instead of the hyperscaler threat and all the Amazons, the Microsofts, the Googles pushing out, it was much more about the eSIM. So a lot of the work that we deliver with eUICC, multi-IMSI bootstrap was really the driver, and we see that continuing to happen. It’s playing a much greater role. The SGP.32 standards, for example, the pull model, is giving the enterprise a lot more control. So I think the overarching prediction was correct. The drivers perhaps were incorrect.

Nick Earle:

But that’s all right. We’ll claim it. We’ll give ourselves a B.

Larry Socher:

Exactly.

Nick Earle:

Let’s give ourselves a B on that. On the second one, if I recall, we predicted the rise of private networking along with public networking.

Larry Socher:

And it went a little bit further. It was around roaming to and from private. As private networks grow in popularity, you need to be able to roam to the public network and back from it. So going into a mine onto private and then leaving that mine and going onto the public networks. And we are definitely seeing a lot more interest. We actually tested roaming to and from private with Ericsson and AT&T as well as Nokia and AT&T at PlugFest, which is a utilities industry show. So there’s definitely much more interest in that. I think the thing we probably got wrong was the timing. While private networks have tremendous promise in the industry, I think just the operational challenges, they’re taking a long time to get in the market. So we’re seeing them in mines. We did work with TELUS and a Canadian mining company, but we’re not seeing the growth and the adoption of private networks. And I think it takes a long time to operationalize that. So the roaming is happening, but the adoption’s a little slower than anticipated.

Nick Earle:

I’m reminded that there’s one of those great quotes about technology world in general is that most people underestimate the importance of new developments and think that they won’t really make a difference. And actually, they make a huge difference, but they overestimate the speed at which they’ll arrive. And I think that’s an example of it. The third one is kind of, in a way, is in that bucket as well and multi-RAT, multi-radio access type, because we’ve talked about operator agnostic connectivity, it’s part of our DNA as Eseye, but we’ve been talking a lot more about RAT agnostic connectivity. So how would you mark our scorecard on that one?

Larry Socher:

I actually think we’re doing pretty well. People are adopting multiple radio technologies, but not necessarily in the same device. So we’ve got a customer, Telli Health, who has both cellular connectivity, which we provide and then they also support LoRaWAN in other devices that they’re using on Native American reservations in places where it’s hard to get cellular connectivity. So we’re seeing the use of multiple RATs. The real thing that we need to see next is really putting it all together, multi-radio access types on the same device. And what’s interesting there is while we’re not necessarily seeing those massive deployments yet outside of maybe the iPhone and smartphones, we’re having a lot of conversations with customers. You are like, “Hey, if I can use a WiFi radio and a cellular radio, I can now operate in the home, use the home broadband network and then go onto the cellular network when I move about” So we’re having a lot of conversations and customers are building towards it, but a lot of the deployments now are single RAT, but using different technologies based on different devices.

Nick Earle:

It’s clearly coming and you can see that in the market. And talking about that, going to the fourth prediction, we talked about consumer enterprise convergence. And you really can’t see that coming because we do these reports after CES in Las Vegas, and I know we recently returned from CES. So how is consumer enterprise IoT convergence getting along?

Larry Socher:

This is starting to happen pretty quickly. I mean, probably if I look at home energy was one area we identified and they’re getting EV charges in the house, which is the biggest draw on the house. We’re starting to see a lot more integration activities there and seeing a lot more going on with home energy. Remote patient monitoring, we do a lot of work because of our secure resilient connectivity remote patient monitoring. Customers are like Telli Health and Biofourmis. And there we’re definitely seeing a lot more interest in elderly care and there’s much more devices and that’s starting to proliferate. And then finally one, I mean, there was a great announcement last year of Google and ADT teaming together in the home security market where you coupled ADT’s enterprise secure home security services with Google’s do it yourself pack and their Nest products and stuff like that. So we’re clearly seeing it happening in all three areas that we predicted and that seems to be accelerating.

Nick Earle:

So the fifth one, and actually let me do the fifth one. The fifth one we said it’s going to be the year of the device. It’s all about the device. And actually, say spoiler alert, but the predictions report’s already on our website. We’ve really doubled down on this one because that one, if anything, we underestimated the importance, I would say, of the device in IoT. There was a lot of announcements around there. And again, from an Eseye perspective, we released AnyNet SMARTconnect, which we talked about in a previous podcast SMARTconnect being… There is a great one with PharmaWatch where SMARTconnect has increased their connectivity dramatically from about 93% to 99% where intelligence is at the edge. And we got that one right. If anything, we underestimated it.

And that allows me to transition right now into the 2024 predictions because we’ve taken a different approach this year and we’ve said this movement of the importance of the device, even to the point where the intelligence will be driven from the device in and everything will be focused with the device at the center, we think is really, really fundamental. And we’ve broken it down into three distinct areas. But maybe, and we’re calling it the year of the IoT intelligent edge. So before we get into the three main predictions, do you just want to say a few words on what we mean by 2024 being the year of the IoT intelligent edge?

Larry Socher:

Yeah, and I think to take a look at this, you really got to go back to… And in my mind, in 2016, Gartner published a report, and I think it was Santhosh Rao and he predicted that by 2025, 75% of all enterprise data will not only be produced outside the edge, but also processed out there. And I think the reason that’s really important, I mean, it’s essentially a natural derivative of Moore’s Law where price performance compute doubles every two years. And as that happens, you can all of a sudden, on a much more inexpensive processing and stuff, get a lot more data at the edge. And this is the idea of having smart sensors and all these things that can produce data as a part of the IoT world.

And that trend has been happening for quite a while. I mean, we’ve actually kind of motivated the trend about the hyperscalers moving into the edge more aggressively. You saw it with Amazon and Greengrass and Microsoft and Azure Stack Edge. So we’re seeing a lot more focus on intelligence happening at the edge and not only in the center of the cloud. And I think that really lays the foundation for IoT just changing where things are processed. It’s not just having a sensor send data into the cloud, but you can do a lot more functioning and processing at the edge. And if you take a look at this year’s predictions, they really build on that trend. I mean, we’re a year out now, we’re in 2024. The prediction was in 2025. Three-quarters of our data would be actually processed at the edge. The intelligence would actually be there.

So a lot of our focus on this year is really, as we’ve got these devices with increased intelligence, remote patient monitoring device, a video surveillance monitor that can now have AI on it, how much of that processing happens at the edge? How much happens at the cloud? And then what is the connectivity that’s needed to connect that edge to cloud? And that’s really the focus of this year is not just that focus on the device, but the device intelligence that’s needed to first process, but then distribute that intelligence and put it in the cloud where it can be analyzed and drive business and commercial outcomes. So that’s the foundation for it.

Nick Earle:

So let’s dive in. Let’s first of all give everyone a roadmap of what we’re going to be predicting about as I said, talking about, actually predicting as well. So we’ve got three this year. We had five last year. We’ve got three this year because we think the device is at the heart of everything as you said, device to cloud. But we’ve broken it down to three separate ones. So do you just want to give a very quick description at a high level of each of the three and then we’ll go deep on each of the three?

Larry Socher:

Sounds great. And they all interrelate to each other probably more so than previous years. So the first one’s really around smart connectivity and how it links the intelligence securely and reliably from the device all the way to the cloud. And you’ve heard us talk a lot about device to cloud. So how do you combine the intelligence and realize, “Hey, I’ve got certain processing that happens on the device, I’ve got some on the cloud, how do I connect it in a meaningful way?”

The second one then really focuses on that connectivity, providing feedback and APIs to the application so they understand the connectivity and allows them to make decisions on where to process. So how much processing should I do on the device and how much do I do in the cloud? And a lot of times that may be a function of the type of network connectivity, the cost of that connectivity, the reliability of the connectivity may allow me to process at different locations. So almost this network awareness that can be given to the applications to make more intelligent decisions on where to do that.

And then finally, as our business cases become more mission, more life critical, maybe more transactional. It becomes much more important to protect the data, to protect the integrity of the solution, the end-to-end communications, to enable compliance when it’s health data, so HIPAA, etc. So it’s around how do I do this in a way that I can secure it and have the audit ability to enable end-to-end trust all the way from device to cloud. And they’re all interrelated, but all of that takes connectivity intelligence on the device that really enables all three of those trends.[LH2] 

Nick Earle:

All right. That’s the high level. So let’s take a deep breath and we’re going to plunge in. And your first prediction, which as I say for the listeners, the predictions report is on our website, probably being downloaded as people listen to us perhaps. The first one’s called smart connectivity software to link the edge to cloud. So can you go into a bit more detail on what this is all about in terms of what’s going to happen?

Larry Socher:

Yeah, and I think the simple way to look at this is understanding, as I just said, if I can do much more processing at the edge, but my real decision factors and any business function or healthcare function is actually driven by the analysis in the cloud and something triggered by that, how do I balance that intelligence on the edge? And I think a great example of this would be like in a water sensor. So let’s say I have a water sensor and I do have some ML on it, so your machine learning because I can now do that as price performance compute comes down and I’ve got smaller machine learning models. That sensor can now look at the temperature, the humidity, the amount of water in that room and start to do some analysis and say, “Hey, it’s trending wetter, so I’ve got an issue. There may be some level of flooding. The room’s getting more humid.”

Now, I need to be able to figure out, “What do I do with that data?” If I’m doing that on the device, what’s the threshold, I need to send a message up to the cloud? Have I hit a certain threshold in order to transfer that? And a lot of that’s going to then require the connectivity into the cloud-based system. That would then be the thing that does further analysis and sends me an alarm saying, “Hey, you may have a potential flooding situation in your basement or in your building.” So there’s an example of just how to connect the intelligence on the actual device itself and actually get seamless connectivity into the backend processing that does the analytics and actually drives a business function, in this case, notification to avoid a flood.

Nick Earle:

So let me ask you about that because I’m old enough in this industry to remember. We’ve heard this story before. I mean, even when we went to three -tier client server as some people called it, five-tier client server, but the point was that intelligence was moving to the edge. The edge at that point was the PC, but there were applications that ran on the PC, and then you said, “No, we’ve redefined the edge. The edge is now the mobile phone.” But, of course, there are applications that run on the mobile phone. There were still computers really. And then you get the IoT device and you’ve said something similar, the intelligence is going to be at the edge. The application, the choices, the logic is going to be processed at the edge.

But the point is that how can that happen when IoT devices aren’t computers? I mean, every IoT device is different, every IoT device is using different sensors, it’s using different components. It’s unique in many cases. I mean, you get generic trackers, and cameras, but most IoT devices certainly that we’ve seen have been designed and built for the use case. So how do you get the intelligent processing to be device resident when the devices weren’t designed frankly to run the applications? And what capabilities are there now in the market to be able to have the application resident within the device?

Larry Socher:

Well, starting off, I mean, you just go back to Moore’s Law. So even though Moore’s Law was finally broken after decades, and this is what price performance compute doubling every 18 months, it’s probably pushed up to about 24 months now. But all of a sudden these devices can be produced at very low cost with a significant amount of processing. I mean, so you look at the latest ARM processor. I don’t need a serious NVIDIA GPU to do ML now. I’ve got tiny ML and other models that can actually process at the edge. So that processing ability of those devices to economically process at the edge has been happening for ages. And the technology has been getting so much better with innovations like tiny ML. Even at Apple right now, if you’ve seen their recent announcements talking about taking the large language models of generative AI and putting it on the phone.

So the foundation has been laid for quite some time, the real challenge is there’s only so much I can do on that device. So take that water sensor, if it’s got a problem outside of maybe sounding an alarm, if it’s got one, if I’m not home, that doesn’t help me. So I really now need to push it into the cloud, have additional processing and notification to wake up. Great example. You mentioned AmericanPharma who have that PharmaWatch product. They have a lot of intelligence. And what PharmaWatch does, by the way, it really tracks environmental conditions for healthcare. So it’s anything like organ transplants, human embryos, they do COVID vaccines where the environment is very important.

Now, they’ve got sensors monitoring those devices. And if they see a condition where, “Hey, it’s getting too humid, etc. The baby could be at risk or the embryo could be at risk,” and they need to do something now. If that’s in a truck or on a plane, I may not hear an alert if I’m the driver, etc, or if I’m a pilot. So I now need to do a notification into the cloud and say, “Hey, look, you better go take a look at this and make sure that we are not about to lose the very precious cargo.” So I really now need to be able to securely connect it and reliably connect it all the way back into the cloud and into some processing that can really say, “Hey, these are the actions I need to do to make sure that I don’t jeopardize that solution.”

Nick Earle:

And SMARTconnect, I mentioned that earlier and we’re allowed on our own podcast to talk about our products. And SMARTconnect actually was designed specifically for that, right?

Larry Socher:

Exactly. I mean, SMARTconnect was actually designed to do the network select and the optimization to simplify the connectivity from that device all the way into the cloud. So it created simple set of APIs, handled all the complexity of the modem communications, etc. But interestingly enough, I think about a year and a half ago, Rogers, one of the Canadian operators had a 19-hour outage. And during that outage, the radio access network was up, but the data plane was down. So a lot of the IoT connectivity, the modems thought they were talking, but didn’t realize that the data plane was down and essentially lost connectivity for 19 hours. We were running SMARTconnect on the PharmaWatch devices. And it realized because the end-to-end data plane was down and it couldn’t communicate with the backend services, it actually recovered and actually recovered connectivity securely, was able to notify them in the case of any anomalies so that none of that transport was at risk during that period. So that was a great example of that intelligence of making sure that the connectivity maintained its backend connectivity in order to not lose the payloads.

Nick Earle:

To put it another way, we mentioned this in the previous podcast, is that for 40 years, the switching intelligence was based in the individual MNO. They weren’t going to localize on their network, they chose which other partners to roam onto. And then for the last five or six years, the switch has moved up one level to the MVNO. The MVNO has the switch and then chooses which operator, especially with SGP.32 that will accelerate. 

But actually, what you are talking about there is that in the case of the AmericanPharma, environmental monitoring device, and others, you mentioned Telli Health as well, you’re talking about the switch is resident within the device. That’s a pretty big architectural change. I mean, that was back to my intro, that’s like saying, “No.” You do realize the PC and the phone, they can run their own applications. The moment the applications can be resident on the edge, the new definition of the edge, a lot of things change. And so the example of Rogers outage is the device was intelligent enough to switch without having to talk to the IoT platform, and that’s why they’re the only company that switched when that outage happened.

Larry Socher:

And by the way, that was a great way to describe it. I mean, if you think about what we talked about last year about the SIM giving the different option to switch to different networks, I mean, here it’s really the application getting control. So give the ability to the application to take control of its connectivity and communicate when it needs to and make changes if it has to switch over. So that’s a great example.

Nick Earle:

And when you open up APIs and have a developer ecosystem into the application logic, you can see the possibility for an app store type capability in devices that’s agnostic to the CPU.

So, Larry, which brings us to the second prediction, and it’s got a really long title, but we’re going to try and simplify it for everyone. It’s called smart connectivity powering distributed data processing for IoT and laying the foundation for 5G. So, this is an interesting area because this really perhaps if I take a step back, we go to the conferences all the way around the world, the telecom conferences, and webinars, and seminars, and everyone’s talking about 5G. And they’ve been talking about it for a long time, but they talk about it from the operator out, which operators are how far along the road of implementing the operator’s 5G strategy.

But the moment you get eSIM breaking the lock, you then get IoT devices using multiple operators, which then makes you think, “Have we got the model upside down?” I mean, can we actually implement a multi-operator 5G strategy? Because they’ve all got to do it independently. Or is there some way the device can actually play a really important role in things like quality of service, network slicing, etc . So this is a pretty bold one which says maybe the best architecture model is to start looking at doing things both outside in as well as inside out. So, Larry, with that intro, do you want to just unpack the second prediction?

Larry Socher:

Yeah, and let me do it in two steps. I’m first going to talk about how the connectivity intelligence can give network awareness to the application to help it decide where to process data, and then I’ll talk about the ramifications for 5G. So if you go back to the first prediction, it was really about just getting connectivity from the device into the cloud to where it can do decision processing. To better understand the second prediction about enabling distributed data processing and helping it decide where to process, a great example of this is the video cameras that you’re seeing in cars. So if you think about it if you’re in an Uber, the security cameras you see in a lot of fleet tracking and management, and not only do they keep track of safety and security in the car, is the driver awake? But they also then typically have what is referred to as an ODB interface to get instrumentation from the car’s engine. So they can do predictive maintenance, they can do stuff for insurance and tracking.

So we’re spending a lot of time with those video camera providers. And here, going back to the multi-RAT example, if you take one of those video cameras and you put both a WiFi radio in it as well as a cellular radio that talks to terrestrial networks, but uses the new 3GPP non-terrestrial network satellite capabilities. So these are modems that can simultaneously talk to terrestrial cellular as satellite networks like Skyler and Sateliot. So, with two modems in one of those video cameras, I can get a scenario where the driver is out on the highways, they have terrestrial cellular coverage, and I can send up a video image every few minutes and an update to the cloud based on the cost of that network. It’s a reasonably affordable network, but I’m not going to stream all my real-time video because it’s just too costly to do that.

In the case when they go up into the mountains or in the forest and they no longer have terrestrial coverage and they’re on a satellite network, that is an extremely expensive connection. So the application itself needs to know, “Hey, I don’t want to send a picture every five minutes. I only want to use this network if I have an emergency situation. I’m in an accident, I need to dispatch for support, the driver’s falling asleep and someone needs to be notified.” So just emergency situation. However, when that car comes back and it goes and parks at home in the garage, I now have a WiFi network with home broadband that’s paid for, it’s very high speed network, I could now upload all of that video and instrumentation data to the cloud for ongoing predictive maintenance and analysis on the driver performance.

So there’s a great example that the actual device and application will send different amounts of data depending on the capabilities, the bandwidth, the latency, and very importantly, the cost of that network. So increasingly, we’re starting to see more of those use cases where the intelligence software needs to not only select the network, but notify the application on the capabilities that allows it to decide where to process and when and how much data to send.

Nick Earle:

So that starts to sound like 5G, doesn’t it? Because you’re talking about quality of service, context specific quality of service, network slicing giving different priorities to different applications. There’s the URLCC movement there with 5G, but the point was that you described that from the device out, not the network in. And is that the sort of big bold projection here?

Larry Socher:

It is. We keep talking about orchestration and network slicing in 5G, but the reality is it’s the device that knows when it needs to use the network, what resources it needs, and it needs information on about what’s that cost of the resource. So the idea here is that you start to look at all the 5G, ultra reliable low latency communications, URLLC, which I really believe is the promise of 5G, and you often refer to network slicing and the radio and access network to enable that QoS. You want the device to be able to signal that, “Hey, I need to do telesurgery, I need these resources now,” and signal it, then ask for those resources, and also understand the cost of those resources. If they’re extremely expensive, how much do I want to allocate?

So you really want the device to trigger that and request it and then the network to be able to respond and not just respond in the radio network. We hear about 5G URLCC and the network slicing, but now it needs to be configured all the way through the software-defined network that connects that device into the application that’s supported in the cloud and to the cloud securely reliably with end-to-end QoS. So the device is really playing a more active role in the signaling and requesting of that, working with the backend system to do the end-to-end orchestration to enable that.

Nick Earle:

And I would say as a final comment on that, you say working with the backend system, I would say working with the backend systems, plural, because in a world of eSIM, if you have 1,000 devices, you could be using 10 different networks because of eSIM and URLCC switching. And so actually, you then think, “Well, how on earth could I implement those capabilities without having it driven from the device in as opposed to network out, because the network out, everyone’s going to be implementing 5G at the network level at a different speed and maybe in a different way. And how do you glue all those things together? So, we think the device is going to play a huge role, but the point I would make before we move to that third prediction is this is a different way of thinking about things.

I mean, of all the 5G conferences I’ve been to and speeches I’ve sat through, people don’t start off by saying, “It’s all about the device.” So, we think it’s logical, we think it’s out there. This will take a while to roll out and it’s certainly radical, but we do believe that, in conjunction with the work that the operator’s doing with 5G, this is a hugely important part of making it real. So, we will see how this one plays out. This one’s going to be interesting in particular how this one plays out in 2024.

Prediction number three, and it was interesting you talked earlier, you talked about security and compliance, and very interesting word you used, which is trust. Now, in all the times that we’ve been doing IoT data and predictions, we haven’t had the word trust before. In other words, it’s very much fashionable now with AI people saying, “Can I trust what I’m looking at? Can I trust what I’m seeing? Can I trust the source of the data?” It’s a very hot subject. But in terms of IoT and trust and why it’s important to be able to trust and to be compliant, it’s a growing area. So let’s talk about that. The third one is called smart connectivity as the foundation for device to cloud security, compliance, and trust. So can you just lay that out for me and maybe use another simple example?

Larry Socher:

Yeah, and I think to your point, we haven’t been talking about trust, but it’s actually been around for a bit. If I take a look at a lot of our customers, because we do secure resilient global connectivity, we support a lot of use cases. I mentioned healthcare, Telli Health, Biofourmis, etc. We do a lot of payment systems. And each of these have very strict regulatory and data sovereignty, GDPR and other type of requirements that they need to reach. So actually, it has been somewhat implicit in some of the work we’ve been doing for quite some time supporting a lot of these standards and enabling that. But we’re starting to see that almost get taken to the next level on that.

One of my favorite examples here is around carbon trading. So if you’re familiar with the carbon trading markets, they’ve actually been subject to a lot of different fraud. And let’s say I have an EV charger, an electric vehicle charger, and I can use that to get carbon credit. So turning that into financial transaction. In order to do that, I first need to make sure that the device has all the right authentication, authorization, encryption, all the security and capabilities, but then it becomes very important that I get an audibility trail in order to support that. And there’s a whole bunch of different standards, similar like we’ve seen with HIPAA in healthcare, PCI in the financial markets. The ISO has been driving a set of standards that really control all that authentication, authorization, and auditability that’s needed to support it.

So you really need to be able to get end-to-end device to cloud security and trust in order to support those standards. And having historically been the biggest fan of blockchain technologies, this is actually a great application for blockchain where you can really get that end-to-end trust all the way from the EV charger that is deserving to those credits all the way back through the network and into the system to make sure it’s there.

So it’s really laying the foundation, having the intelligence on the device to do all that security, to do all the auditability, to protect it, to support all the standards, and then making sure that’s extensible through the network. So, not only when I enter the network as a mobile network operators through the APNs, but I now need to have an end-to-end network that can make sure I route according to that compliance. So if there’s data sovereignty or any regional requirements that need to be met, making sure I have end-to-end security all the way from that device into the cloud with full auditability to support that use case.

Nick Earle:

And we think this one is in the very early days. We know that the whole, as you say, carbon credits, the drive to keep global temperatures below 1.5 from pre-industrial levels, which we seem to be losing. So, there’s going to be even more focus on that. And, of course, the price of a carbon credit varies actually primarily, well, in two factors, the use case. So a wind turbine is more valuable carbon credit than a small solar-powered cooking stove. But also can you trust the data? And the more you can trust the data, the more the economic model kicks in and the more valuable the carbon credit is. So there’s a huge financial incentive to get trust end-to-end from the device to the cloud into it.

And when you’re going over the public network and you have different APNs and you’re going across different operators, it’s a real head scratcher. And that’s something that, of course, we are working on a lot. And again, SMARTconnect, from our perspective is absolutely key to that because we believe that you can only do that if the trust is centered. The security and the trust goes from the device out because it’s the one common denominator that touches all the other points in that architecture model.

Larry, we’ve spoken for quite a while here. We’ve given people a glimpse of the predictions. So just to sum up, we think it’s all about the device, which was the fifth one last year, but actually we really think it’s all about the device this year and we’ve broken it down to three subcomponents. We’re out there, public, we’ve stuck our neck out again. We’ll see how we do, but we do see a lot of innovation around these three areas and a lot of people wanting to talk about it. And this really does take IoT into a different area. The idea of intelligence being device resident, the idea of it’s not just agnostic connectivity, but across the operator, but agnostic RAT. I mean, I remind everyone, cellular is only 13% of the worldwide connectivity market. You think it was a lot bigger from all the conversations around it, but it’s only 13%. So, there’s a lot more that’s multi-RAT out there.

And then the idea of how do you do really complicated things like 5G, trust, auditability in a world where eSIM has changed everything because suddenly an IoT use case. Devices can hop between operators, especially devices that move. And how on earth do you impose architectural standards, security, compliance, and trust across that?

So a lot going on, and that’s why we wanted to do our annual podcast series. And we’ll see in a year’s time, I suspect we’ll be in a similar sort of situation. Some of them we will have probably got wrong, frankly. Some of them will say, “No, we got that one right.” And then probably somewhere in there we say, “Boy, we should have emphasized that one even more because that one accelerated.” But we won’t predict now how we’ll finish the year. We’re just saying this is the ones that we think.

So thanks, Larry. Thanks for the time and thanks to you the listener, or indeed the viewer, if you watch this on YouTube. We have a pretty loyal customer base now. We got a lot of messages from you, so thank you very much. And we know a lot of people forward this onto other people, so we encourage you to continue to do that. So you’ve been listening to IoT Leaders, this is episode 42. As a reminder, 41 should be out by the time you listen to this was Amazon, which is a great podcast. And 40 was our two founders talking about how you build an architecture model for IoT. So, we’ve had three great ones on the run here, and we intend to do many more as we go through 2024. So, Larry, thanks again for doing this – report card available in 12 months’ time, and thanks to the listeners. And any feedback is welcome, and we’ll see how the market plays out. Thanks and thanks for listening to IoT Leaders. Cheers.

Resources