Precision is Everything: IoT Asset Tracking and Monitoring

If someone claimed they could tell you where anything is on the planet within 10 centimeters of precision, would you believe them?

CEO and Founder of Link Labs Dr. Bob Proctor’s goal is to deliver on that claim. Moreover, he’s got both the credentials and the detail to back it up.

Bob joins the podcast to offer insight into what Link Labs offers the IoT landscape, including:

• The challenges of high-accuracy asset tracking and monitoring
• How Link Labs is overcoming these challenges
• How IoT and cellular connectivity converge
• The huge potential use cases that come with a solution breakthrough

Tune in to learn more about the latest advances in the world of location tracking.

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Transcript

Intro:
You’re listening to IoT Leaders, a podcast from Eseye that shares real IoT stories from the field about digital transformation swings and misses, lessons learned and innovation strategies that work. In each episode, you’ll hear our conversations with top digitization leaders on how IoT is changing the world for the better. Let IoT Leaders be your guide to IoT, digital transformation, and innovation. Let’s get into the show.

Nick Earle:
Hi, this is Nick Earle, the CEO of Eseye, and welcome to this episode of IoT Leaders. This week, we’re going to talk about physics, which isn’t a subject normally associated with IoT, but this week’s guest, is Bob Proctor, the founder and CEO of Link Labs in the US. And he’s going to talk about his background, he has a big background from Cornell, multiple degrees from Cornell in physics, and how he’s used that knowledge to actually solve, indeed have a breakthrough on the whole issue of tracking and location of devices. And although a lot of people are making claims in this area, you’ll hear in the podcast that’s coming up, he talks about the fact that he can get down to 10 centimeters for any item indoor or outdoor. In fact, he uses the phrase a couple of times, he says, “I can tell you where everything is on the planet to within 10 centimeters.” Which is quite a bold claim, but there’s a lot of technology in this podcast, a lot of capabilities that he’s put into his devices, going back to his physics background and his team who were very, very technical.

They are an Eseye partner and we’ve been working with them on how to make that global, particularly with regard to the cellular element around the world. So this is a great one in terms of the technology and how it’s advanced enough to now make finding things within 10 centimeters possible at the right price. And then we talk about the use cases, which have been out there for a long time, and the use cases that and the types of companies that would really want to use this, of which there are a lot. So I think it’s a great podcast, Bob’s a very, very knowledgeable guy, he’s been at this for years. He knows a lot about this subject and we go into this in quite a lot of detail in the podcast that’s coming up. So with that, I’m going to hand you over to my discussion with Bob Proctor, the founder and the CEO of Link Labs. Hello Bob, thanks for joining me today on IoT Leaders podcast.

Bob Proctor:
Thanks for having me, Nick.

Nick Earle:
We’re going to get into your very interesting story and your company Link Labs, which you’re the CEO and the founder, and the whole world of tracking and very high accuracy location tracking in particular, which is a big area in IoT. But before we do, we always like to get to know our guest on IoT Leaders, and I was looking at your LinkedIn profile, and one of the things I was thinking, it must be a typo, I don’t think it is, you seem to have really liked Cornell University because what I could see, you kept on doing degrees in Cornell. So maybe just for our listeners or indeed any viewers on YouTube, maybe just start off with how did you get started? And I know the answer is Cornell and so you were there for a while, right?

Bob Proctor:
Yes, I was in upstate New York, where Cornell is, cold, rainy, grey Ithaca, for over a decade, both undergraduate, a couple of master’s degrees, and a PhD in applied physics. And I just had this innate curiosity to want to know how everything worked up and down the technology stack. And I thought I’d be an academic, and then I realized academics don’t make any money, so I’m also very entrepreneurial, so a pivot out of academia upon graduation.

Nick Earle:
All right, so strong academic background, and I think that does come out of the story. I don’t know to what extent applied physics is being applied, but I guess it is actually in what we’re about to talk about because this is a really very strong technical value proposition. So the company Link Labs you founded, as a reminder, and full disclosure, Link Labs and Eseye are partners, but how long ago did you found Link Labs?

Bob Proctor:
Link Labs was founded in 2014. I was a full-time angel investor actually at the time, and had been mentoring a very senior technical person who was doing a lot of asset tracking and monitoring for tier one nation state problems in the United States, call them the three letter agencies, NSA, CIA, et cetera, et cetera, FBI. And some of those problems are really, really hard. If you’re going to put a tracking device on a bad actor, that’s a life-threatening mission. You want that device to last as long as possible and you have really challenging performance requirements with that. So the core engineering team from Link Labs really came out of working on that and I saw a much larger commercial opportunity. And so I funded the initial step out from custom federal sector work into more standardized commercial products.

Nick Earle:
Okay. There’s probably very little we can double-click on that for security reasons so we’ll skip over that, but it does again show the very strong academic and, in this case, I guess military or I can use the word espionage.

Bob Proctor:
Yeah, I looked for a critical mass of world-class talent as an investor, and particularly technical talent since I have a deep technical background and felt that I had found that with the particular engineering team that we founded Link Labs with.

Nick Earle:
All right, just start with the basics because there’s a lot of people who will be really interested. I mean for us as Eseye, asset tracking, monitoring is one of the verticals that we get the most inquiries about, and a lot of people really don’t know too much about the technologies that are out there and how accurate or, in many cases, how inaccurate they are. They’re often surprised that when they see the little graphic with the dot, this is where your asset is, they don’t realize that it’s plus or minus quite a distance. It’s really not as accurate as you think. So if we start off, first of all, because I know you started off in Bluetooth initially and still do use Bluetooth significantly, but there are multiple Bluetooths, aren’t they? And you really looked at how do you get the accuracy for Bluetooth down to a smaller distance as possible, and in particular looking at the issue of finding things inside buildings as well. So maybe you could talk about that.

Bob Proctor:
Yeah, absolutely. Bluetooth has been around for quite a while and the easiest way to figure out where something is with Bluetooth, there are lots of companies that make, and this was really spurred by Apple and consumer products, you want to know roughly where something is, you have a Bluetooth beacons, it’s called a Bluetooth beacon. And if I had a personnel badge, my personnel badge would beacon say, “I’m Bob, I’m Bob, I’m Bob.” And to figure out where I am, you would just put a bunch of listening devices around that would then say oh, I heard Bob, he must be here. We call that proximity based location. It’s based on how loud do you hear something and it’s useful for three to five meters of accuracy.

But if I want very precise indoor location, I have to put a lot of listening posts around. Those listening posts can’t be coordinated, they don’t know a priority where Bob’s going to be, so they all have to send their information back to the cloud. So you’ve immediately faced this problem of if I want to get to meter-level indoor accuracy, I’m going to need a listening post every meter. I’m only reporting location of Bob was next to this thing, it’s like a Snap 2 type of location technology, and that becomes very problematic. So if I add a lot of listening posts, the volume of data grows exponentially because now if I want 1-meter accuracy and I can hear something for 10 meters in a 10 square meter place, 10 by 10, that’s 100 listening posts. First of all, that’s outrageously expensive, and then I’ve got a hundred times the data volume going back to the clouds all saying I’m Bob.

Nick Earle:
Business case collapses, I guess.

Bob Proctor:
Business case completely collapses and now go figure out which one of these hundred things was Bob nearest to. So you can’t get to anywhere remotely close to one-meter accuracy, which now if you said, “Well I want to find a pallet on a rack in a warehouse to get the right pallet of goods,” well I can’t do that with Bluetooth proximity. It’s just not accurate enough, and the infrastructure, and the data, transport costs just grow exponentially if I try to drive it to that kind of capability. But it’s good for what room am I in, am I in the receiving area versus the back of the warehouse, so I can answer simple business questions but not the level of detail that, if I’m running the warehouse I wish I had, which is can you just tell me where it is on what rack in real time?

Nick Earle:
And that’s a problem that you solve. So what have you done, as a company and in terms of technology, to break through that problem?

Bob Proctor:
The first was to recognize two or three things, one is what I just described. I’m basically using a device, that listening post, for two functions simultaneously. One is I know where the listening post is, so it’s giving me location information, the other one is I’m using that listening post to send the information back to the internet, to the cloud. And if you break those into two separate devices and two separate functions, one providing location information and one providing back haul, you actually have to change the architecture of the solution. So rather than I’m Bob, I’m Bob, I’m a talker, I switch it and say I’m actually going to listen and I’m going to put location infrastructure around a constellation of what we call location beacons. And this is where the analogy to GPS is quite strong, you have a constellation of satellites that are providing location information and sending that out regularly. The device now can say, “Where am I? What am I closest to?” And I only need one piece of infrastructure covering a much broader area that says, “On your path to the internet.”

I have to then put the location algorithm on the edge device inside a Bluetooth chipset. So our first insight was split location reference information away from back haul, first key insight. Second key insight is gosh, these Bluetooth chipsets, I’m probably showing my age, all those years at Cornell, if you think about it as Bluetooth is our radio, that’s correct. But I tend to think of it now as the first laptop I had back in the ’80s, early ’90s, when they first started coming out, it has memory, and has got arm core processor, and it’s got a lot of compute power, and there’s excess power that’s available inside that chipset. And so by determining the location not in the cloud but on that chipset, now I only send back information that is fundamentally useful. So we’ve generated a lot of IP on reversing the roles of Bluetooth. I’m a listener rather than a talker, I’m doing the location algorithm inside of Bluetooth chipset, as an example. I still have a problem in that I am basically still reporting what is the loudest.

So I can now say, well I’ll put a thing that beacons pallet slot one, pallet slot two, pallet slot three in my warehouse example. And the challenge with that is if they’re going to be battery powered, do I really want to go, if I have 10,000 pallet slots, replace, 10,000 sets of batteries every three to five years? Most warehouse operators would say, “No, sorry.” If I’m going to run those cabled with power, it’s still just a lot of infrastructure. And so it’s useful for a number of broader set of use cases, but it’s not the holy grail of indoor location, if you will. What we have created in our latest generation of technology basically runs just like GPS, it’s called phase ranging. So instead of listening to where I am the loudest, I calculate the distance to one of those location beacons on the tag. And so I can say I’m five meters from that one, and three meters from that one, and seven and a half meters from one over there. And those all create circles essentially, or spheres, in space, and where they all intersect, I can determine where I am.

Nick Earle:
That Venn diagram. Does that increase the accuracy by narrowing down?

Bob Proctor:
Yes, very much so. And there’s a lot of algorithmic and more physicsy tricks to do that, but we get down to roughly a meter. We’re sub-meter accuracy 90% of the time in even very complex, harsh environments. The other challenge with Bluetooth is, and I say it’s which is the loudest, it turns out it’s a very noisy environment. There’s a lot of RF out there from all kinds of devices, and so if you look at just what happens to Bluetooth signal strength, it’s jumping around by factors of two, three, four, five, six in any given period of time. So figuring out what is loudest involves a lot of averaging and just algorithmic work, and so that’s also inherently challenging with that type of methodology. So determining distance and then getting the intersection of spheres, we get to very high accuracy.

And the nice thing about that is if you go back to the warehouse example, now I don’t need a reference location beacon at every pallet slot, I just need one every 10, 20 meters, a grid in the ceiling, and I can determine the intersection, and I can figure out the proper pallet slot with very light infrastructure. We also have done that in a way that is fundamentally more power efficient. So if I’m going to now be tracking 10,000 pallets in a warehouse, how long does that tag last is a huge driver of ROI. We try to reduce the amount of power to determine location by about an order of magnitude, so if I have what would’ve been a six-month device turned into a five-year device, that completely again changes the ROI. That’s a lot of what we focus on is how do I make this very low cost and affordable, this technology very accurate and very affordable. The combination of affordability and accuracy allows you to have the ROI to have the business case really be strong for industrial companies.

Nick Earle:
Okay, so let’s deal with the use case. You now can see the thing using the overlapping Venn diagram equation principle. I’m sure there’s a snappier name than that. You got it down to within a meter, and I know we’re going to talk about getting it down further than that later on, but for the moment, let’s say we got reduced from many meters to one meter, which in itself is maybe an order of magnitude better. You got battery life management, which is absolutely essential, as you say. And so what happens when the item gets picked, I don’t know, it goes on the back of a truck, and people don’t just want to track inside, but they want to track outside? So then we go into the whole world of GPS and cellular, which of course we started talking to each other, and then location tracking for things that are outside, it’s got similar problems, hasn’t it?

Bob Proctor:
Very much so.

Nick Earle:
I was referring in the opening, standard GPS accuracy is a lot less than people think, isn’t it?

Bob Proctor:
It is, and it’s corrected. So anyone that’s used their phone for walking around finding directions in a urban canyon environment, as an example, it’ll put you on the wrong side of the street, or the wrong block, or have challenges. And that’s due to reflections, for example. In the tech industry, that’s called multi-path. You get a reflection, you suddenly think things are farther away.

Nick Earle:
Yeah, you get out of a tube station in London and you see the little green funnel thing says you face that way, so you start walking that way, and then about 10 steps down the line it flips.

Bob Proctor:
Yeah, it says whoops, wrong way.

Nick Earle:
And that’s something happened. It’s a reflection.

Bob Proctor:
Yeah. So you’re dealing with radio signals that inherently have a lot of, not only noisiness but reflections. And then the other thing with GPS is you get a lot of atmospheric distortions. So the way GPS works is similar to what I described with what we determined location, the Venn diagram analogy and warehousing, that’s what you’re doing on earth with a series of GPS satellites, your device is calculating the distance to each one and looking at the overlap with them. That’s only accurate to 10 to 30 meters in a typical use case. The reason you get better is you probably might recall, I think it probably is just by default today, turn on Wi-Fi for better location accuracy.

What’s happening there is, the way I think about it is determining location is always in relation to a reference dataset, but GPS, that reference dataset is the GPS satellite positions. But you can also do that with Wi-Fi and signal strengths. So in the background, there’s lots of companies that have been using everyone’s cellphone, coupled with other data, to say ah, there’s a fingerprint, if you will, a radio fingerprint that’s always around that says if I can hear all these Wi-Fi signatures with various signal strengths, I must be here, and I’m not using GPS at all. So now if I use GPS with Wi-Fi or RF fingerprints, I can actually get better location accuracy. So you can use them in combination, or you can use one or the other.

Nick Earle:
I’m going to ask you a question because I want to know the answer because one app that I use a lot on my iPhone is Find My, and I use it quite frankly to track our family members, let me put it that way. Two daughters and well, first of all, I like the fact that they’ve let me track them, permission based, but I also can track them. And this morning, before recording this, our daughter was coming to our house, our youngest daughter, and not only could I see where she was, but the dot was moving. The little blue dot was moving and I was saying to my wife, “This is seriously cool.” I mean it’s like the Harry Potter Marauder’s map, if you remember that thing. The map with the dots. So I just want to know the answer to the question, that technology, is that what you just talked about or is that something else? I mean it can’t be standard GPS to get that level of accuracy.

Bob Proctor:
The more reference sets of information you’ll use, the easier it is to get more accuracy. So they’re probably using a combination of GPS plus a very strong database. So your phone is not only determining your location to put a dot on the map, it’s also sending information to Apple that says I got this GPS reading, I also got all this Wi-Fi. It’s like when you scan for Wi-Fi networks around you and you see the varying signal strengths, all that data is being sent back up to the cloud and it says, “With this GPS fix, I also have this Wi-Fi set of fingerprints.” The next phone comes along and GPS is off for whatever reason, but it sees all those Wi-Fi fingerprints, it’s like well, I know where you are still anyway, I got the correlation between the GPS and the Wi-Fi fingerprints. If you can average that over a billion phones, you can get some highly reliable information.

Nick Earle:
Yeah. And often the breakthroughs do happen on, that’s where I was going, these mass volume consumer devices. So if we jump ahead, because you’ve talked about a meter and then you talked about atmospheric variations, and reflections, and outside with GPS it can be 20 to 30 meters, but you’re really a company that really understands the devices, and you get down into things like the Bluetooth chips, and you add value to them. And looking at your website, well two questions really, you have an indoor product offering and you have an outdoor product offering, so AirFinder and SuperTag. And you’re also talking about getting down to 20 centimetres indoor and outdoor, which is like holy grail. I mean, again, it’s another huge leap. The business value for getting from a meter to 20 centimeters, it’s not a straight line, it’s exponential and it’s probably exponentially harder. So maybe we can just unpack that. Can you just talk about your company and the difference between what the AirFinder is and what the SuperTag is, and then as we go through that, how they work and what you’re targeting in terms of accuracy?

Bob Proctor:
Absolutely, yeah. And a clarification on my part. So we AirFinder, we really call our platform, which is indoors and outdoors. Indoors, we call it AirFinder Onsite and outdoors, we call it AirFinder Everywhere. That’s the platform. The SuperTag is the primary device that we use within AirFinder Everywhere outdoors. And actually we call it a SuperTag in part because it works outdoors and indoors, it can seamlessly transition outdoors and operate like a tracking device that people might be familiar with GPS-based outdoors, but it will transition into a tag inside an AirFinder network inside a facility you might control, like a warehouse, a manufacturing site, a repair facility, et cetera. So indoors, we establish an AirFinder network and get to this high indoor accuracy, and then outdoors we do the same.

So where we’re going, and you’re absolutely right, is to 10, 20 centimeter accuracy indoors and out. And the goal here, and we’re basically there as a company now, there’s no further science or invention that’s required, we’ll release our indoor product within the next couple of months. Probably by this time this podcast goes public, that product will be launched. The outdoor side, the technology is there, we’re just working through volume production right now on the tag side. The way to think about indoors is that it’s possible today to get 10 to 20 centimeters indoor accuracy with a technology called ultra-wideband. And so the physics, if I go back to my roots, of location is really driven by the bandwidth of the signal.

Ultra-wideband is very appropriately named as ultra-wide band, the broader the bandwidth, the more location accuracy you can get. The only challenge with that is the broader the bandwidth, the more power I need to drive a radio signal through that, the shorter the battery life, and frankly the larger the device I need, so the form factor becomes also a problem for tracking small assets. So with our technology, we basically have now fully, if you will, hybridized or integrated all the various indoor location capabilities into the AirFinder Onsite platform. So I can use Bluetooth out the box with proximity for room level accuracy, if I’d like. I can use the work that we do with the Venn diagram, as we described earlier, with location beacons. We call that phase ranging. Our technology we call XLE for extreme low energy, because it’s very power efficient, and that gives me that one meter accuracy, and then I can use ultra-wideband in the same device to get to 10 to 20 centimeters of accuracy.

Nick Earle:
Excuse me Bob, for clarification, the way you’re describing it is that you can use each of them, you’ve packaged it all into one platform.

Bob Proctor:
Yes, exactly. And the reason is this, today, if you’re a company looking to cure this kind of indoor location capability, you have to choose one. I can choose one, or the other, or another. And the problem with that is when you get into any industry, and I’m sure you’ve seen this as well, I think of it as there’s micro fragmentation or high fragmentation of use cases within an industry vertical. If I get into a manufacturer, I can say I want to do work in process tracking, and I have workstations, and there’s a piece of tape on the floor, and I need to know the dwell time in each workstation, so when I cross that line of tape on the floor, I need to know that. That’s 10 to 20 centimeters of accuracy.

If I’m trying to find torque wrench, and I’ve tagged a torque wrench, and it’s in a 500,000 square foot facility, one to two meters of accuracy is fine, and that allows me to have lower cost tags. Say this happens to be in a cold-chain environment, I want to have a temperature probe, and I need to know the temperature of these things, and they’re actually not moving around much, but that might be a standard Bluetooth beacon that’s also incorporating sensing information. So I’ve got inventory counts, tool tracking, calibrated instruments, work-in-process, condition monitoring, could be temperature, humidity, shock, et cetera. Suddenly, I want multiple location and sensing technologies all available in one platform so I can choose the tag that provides the most benefit and meets the needs of that sub-segment of the use case.

Nick Earle:
And that’s the key, isn’t it? Because there’s a lot of companies in your space and what I’m taking away from this is that many of them will, if not all of them, say, “We’ve made a choice and we’ve chosen this technology,” and then that has these certain use cases, but it doesn’t do the 20 centimeter, the pallet, the wrench, whatever it is that doesn’t need as accurate location. Whereas what you’re saying is with all of your device knowledge and your technical knowledge of you and your team, you put it all into your platform, so you choose the tag for the use case, but you can have multiple tags in the same environment and configure them for the use case, like you say, for the wrench.

Bob Proctor:
Yeah, exactly. I mean if you said you have to do work-in-process tracking of a large piece of equipment across a boundary and simultaneously track a torque wrench in a large facility, the torque wrench has a requirement of I can’t have a giant bulky tag making that a non-usable instrument, I need to have a very small tag. Ultra-wideband is just too big a tag. Or I can take it to a set of micrometers or calipers that are very small hand-tools that I may want to track in my facility because they’re all calibrated for specific production steps. I can’t put an ultra-wideband tag on them, they’re just too big and bulky, but I don’t need 10 centimeters of accuracy to find it.

Nick Earle:
You don’t need it, yeah.

Bob Proctor:
I can put a big bulkier tag, and I say big and bulky, it’s still not unreasonable, onto a piece of work-in-process and I can get the accuracy I want. And in today’s market, you can find a vendor that can do one or the other, but not both.

Nick Earle:
You got the AirFinder platform and different SuperTags for different use cases, so then you find the devices and, in some cases, you pick the devices, and the devices go outside. So now, you’re not inside the factory and you need to back haul the information. And as we said earlier, that’s where we started engaging quite a while ago and you had the issue of okay, now I need to add cellular onto that. So can you talk a little bit about the SuperTag and its cellular capabilities?

Bob Proctor:
Absolutely. So if you’re, again, a large multinational and you’re looking to understand not only your production environment, we start in the heart of the factory typically, work-in-process, and then parts inventory flowing into that facility. So you have the whole upstream supply chain and then you look at customer service flowing down, and distribution. Many complex products are essentially customized to the end customer and they want to know where they are in their transit to the customer site. Those problems quickly become challenges for multinationals of multi-geography coverage capability and the ability to track across multiple countries. And then within a country, no one network operator, as you know better than I do, has enough coverage. I used to say that the problem with cellular networks and network operators is they’ve really designed their networks for people, not for things.

Nick Earle:
The whole infrastructure, and we said this in many podcasts, people took a very successful consumer voice data infrastructure business model, regulatory environment, device environment, and then tried to bend it into IoT, and it just became 800 proprietary silos with no global capability.

Bob Proctor:
Absolutely, yeah. I like to say that the problem with the Internet of Things is it’s things and not people. And things end up in places that people aren’t very often.

Nick Earle:
And they stay there for longer, which means because in the consumer model, if you’re there for three months, you get kicked off because the operator who’s native to where the thing ends up says, “Well, I’ll let you roam onto my network, but after three months you’re out of here because you’ve got to now put my SIM in.” It just doesn’t work. And thinking about your use case, you’d be changing the SIM card on those SuperTags, or your customers would have to, constantly.

Bob Proctor:
Absolutely. And just all the carrier relationships, et cetera. So there’s really three capabilities that we really partner with Eseye for here, the first is obviously the MVNO type capability. You’re managing all the carrier relationships, I don’t have to deal with that, but somebody’s got to put that together and that’s a really valuable service capability. Obviously, there’s the eSIM part so that it isn’t swapping out SIMs, and those capabilities, and the ability to roll through different network operators even in a specific geography. And the third really is you need a team. We have a lot of deep device knowledge, but your team does as well.

You need a team that can really understand these technologies at a very, very granular level. Something as simple as the message specification. We’ll probably talk about how do you get to hyper-location outdoors, well now I have different pieces of information I need to send back up. That means I need to package those up into a message specification on the device level and I need to work with a company like Eseye. And you understand at the device level how does that impact in terms of what I’m actually sending, and how does that flow through your system and get to the cloud to understand more precise locations.

Nick Earle:
Yeah, and we tend not to use the word connectivity, and I think you’re probably the first podcast guest who’s made this distinction. We tend not to use connectivity, people get asked about it, but we call it device to cloud because of that issue, because it’s not the connectivity just to the operator, you really want is the data to the cloud, and it’s very complicated, and so you’ve got two companies with deep device knowledge solving this problem. And in terms of the listeners to the podcast, I just want to bring it to life in terms of what that could mean because with your SuperTag, and we’re going to get into the use cases in a minute because whenever this problem gets solved, the use cases are waiting, they’re out there, and they’re waiting, and they’re huge, but the accuracy that you think you can deliver, again by combination of factors, capabilities, and the ability to jump between operators and have it all be taken care of, what accuracy are you aiming for on the SuperTag outside?

Bob Proctor:
Yeah, so we’re aiming for 10 centimeters outside as well and the technology’s there to do it. So our goal is to basically take the gosh, I’m struggling with the location accuracy out of the conversation for any large industrial company.

Nick Earle:
Make it go away, yeah.

Bob Proctor:
That problem is gone. I can tell you within 10 centimeters basically where anything is on the planet. And that’s pretty powerful when you start to think about all kinds of use cases. I guess there’s two questions, one is how do you do it? And then the other one is well, what are the use cases that you see coming for that?

Nick Earle:
Well, they are the two questions. By the way, I love that phrase I can tell you where anything is on the planet. That should be on your website. But I like that. Can I ask you, to the extent that you can, because I know you’ve got a lot of patents, and this is your secret sauce, and you’ve been working for a few years on this, can I ask you that first question? Then we’ll ask the second question. How do you do it?

Bob Proctor:
Yeah, the how do we do it on the outdoor side, it’s known in the industry. So the problem with GPS is fundamentally it’s atmospheric distortions. So I’m trying to measure the time it takes for a radio signal to go from a satellite with a very known location to my device, and then with basic physics I’m figuring out okay, well how far away am I? But there’s all kinds of density variations in the atmosphere and those types of things that change all the time, so I don’t quite know that distance very accurately to any satellite, and so the algorithm that I’m using to calculate my location isn’t very precise.

The atmospheric distortions are happening over pretty broad scales, many kilometers as an example. So if I took a known reference station, a GPS receiver, let’s say I put one, and this is what, for example, Verizon is doing in the United States and it’s largely deployed, every cell tower has a fixed GPS receiver on it, and the location of that GPS receiver has been surveyed down to centimeters. And so I can then basically look at that receiver and say well, it calculated the wrong distance by a certain amount to a satellite, that error must be due to the atmospheric distortion. So you get essentially a series of correction factors to all the satellites. And so now if I have a GPS receiver that’s within a few kilometers of that cell tower, I can say give me the correction factors and I’ll hone into 10 centimeters.

Nick Earle:
Well, back to that Venn diagram principle again.

Bob Proctor:
Yeah, exactly. If you think about that Venn diagram with standard GPS, it’s like shells, and then I’m just making those super thin.

Nick Earle:
Yeah, and the more you can connect to as the operators, like you say Verizon put one on every tower, then as the weather fronts move through, you’re constantly adjusting and you’re getting this accuracy.

Bob Proctor:
Yeah. Now, if I’m a company where that’s not where Verizon is, I’m in Europe or something like that, a classic use case is yard management. I’m an auto manufacturer, heavy equipment manufacturer, I’ve got a big field that I’m rolling out. We call it sometimes the white Toyota Camry problem is the way I think about it. We’re not working with Toyota or the white Toyota Camrys, but hypothetically I have a production line, it’s missing, due to the chip shortage or something else, a part.

And so I got a field with 500 white Toyota Camrys now with all specific bins. I say oh, the parts came in to fix these 10 cars, go find them. Well how are you going to do that? Even if I’m 30 meters accurate, there’s 30 or 40 cars that I’m trying to inspect that then I’d like to get to this parking spot specifically. So all I need to do now is put a known GPS receiver at the edge of that lot, and know exactly where it is, and have the correction factors available to me, and I can find that within 10 centimeters. So I can get to a very specific parking spot outdoors, as an example.

Nick Earle:
I heard about this case study when we were chatting previously, and I must admit I didn’t realize, it makes perfect sense when you think about it, but I didn’t realize. So because of the component shortages, and we all know China Zero-COVID and component shortages, people still have to make cars. You think that they just won’t make the car, but no, they do. They still make the car, they just don’t put all the components in the car, and they put it in the lot, or the field, or whatever. And like you say, there may be 500 cars, but they only had 490 components, just to explain the use case. So there’s 10 out there that are short of 1 component, but there could be 30 out there that are short of another component, so you’ve got this overlapping problem.

Bob Proctor:
You got it.

Nick Earle:
And so what the car companies are doing is when the components come in, they then go and find the cars and retrospectively fit the component rather than hold up the whole production line. And I never thought about that, but it makes a lot of sense. Of course, they have to manufacture cars. And so the alternative to this is, I guess, a bunch of people who just walking into the field-

Bob Proctor:
Yep, running around trying to find it. Absolutely, yeah. I mean auto auctions are the same way, but also even with finish goods, where there’s not a shortage, we’re working with a large industrial equipment manufacturer has super large lots and most of these products, just like if you got a package on your automobile when you bought a new car, I want the sport package versus the leather interior package, et cetera, those get to be specific to customer. And so when someone’s coming with the car carrier to pick up a certain set of cars, if they inadvertently get the wrong car on the carrier, that’s a very expensive problem because now I’m shipping the wrong car to a customer. They’re not getting what they wanted, they’re sending it back. You got all kinds of reverse logistics, you got unhappy customers.

Nick Earle:
And this emphasizes, and I want to get onto another use case in a second, but this amplifies one of the things that we’ve had on the IoT Leaders podcast is that the cost of the technology is one thing, competency of the technology is another, which you’ve talked a lot about, but the real thing is the business outcome. And people say to us, “How do you justify IoT projects?” And we always say, “It’s not around just the device, can you actually take significant cost or improve the process to create the business outcome?” And in this case, as you say, the cost to the car companies of what you just talked about is probably hundreds if not thousands of times, let’s just use hundreds, hundreds of times more over a period of time than the cost of the technology when you amortize the technology. So this is where you really get the business owner, the CXO level attention or whatever on the use case.

Because we could talk for hours on this, but I just want to get to one more use case, and then ask you question about the future and why you think this is just about to happen. But before I do that, the use case that I’ve heard about, and I think this is perfect for you, is the whole home delivery. I mean it’d be the same in the US as it is over here in the UK. I went round Ocado or one of the companies over here, the distribution for supermarkets, I went around one of their factories, and conveyor belts and automation, but at the end of the day it’s tens of thousands of plastic crates which are all having individual items put in, and then they go in the back of a van, and then the van leaves. And then they get dropped off, they drop off at your house, or they put it outside your gate if they can’t get you to answer and whatever. And tracking those and getting them back, because there’s a cost, these plastic containers, that’s an area that is a great use case for your solution, isn’t it?

Bob Proctor:
Absolutely, and that ties into really sustainability and sustainability initiatives as well. Returnable plastic transport items, if you think of them as just a cost of doing this business, they’re rather expensive and there’s a lot of them that don’t get returned that end up in recyclers or in the waste flow. And if you compare it to something like cardboard, a lot of people say, “Well, why don’t I just continue to use cardboard?” Which has a large waste stream associated with it. But if you change your perspective and think about that plastic container as what I call an IoT platform, I liken it to the difference between say Uber and a FedEx or a UPS.

With Uber, I’m watching that dot on the map, just as you described with your daughter earlier, so I can provide better customer service with that versus I know it left the warehouse, but I don’t know why it never got to the customer. But I can also do things like monitor conditions. For example, the temperature, if I’ve got refrigerated goods, do they go out of bounds? And if I’m going from say a distributor to a grocery store, that’s really valuable information, that temperature information, because I know which product’s going to spoil before other products, which is going to allow me to figure out how to prioritize what actually gets put on shelves. Now, suddenly that piece of plastic as an IoT platform is allowing me to run my business in a very different way than I did before.

I can also start to know things like inventory levels and retail establishments, and I can do delivery route optimization, I can do optimization on the return flow. I can figure out where there’s theft and waste. We had an interesting client that manages rental properties, 90,000 rental properties across the United States, and they had a problem with vacant properties were having appliances in the appliance shortage post COVID disappear, and ultimately we’re able to track the flow of appliances, and figure out where the bad actors were, and solve that problem. We see loss and wastes, delivery route optimization, better customer service, faster inventory turns, understanding the demand side, flowing that back into what do I actually need to produce? The ROI really is off-the-charts when you start to push on this.

Nick Earle:
And we’ve said on a previous podcast, if you just take the chilled food distribution, one of the examples that you gave, I think there’s something like 30% wastage in the supply chain, which is billions of dollars, to put it in context. So the business opportunity for solving these problems is huge, which leads me to my final question, which is that it’s been known for a long time, things like 30% wastage in chilled food distribution, food gets thrown away, or we’ve had the problems of finding the car in the car lot for many, many years, but I know you really believe the time is now. Now, 2023 is probably the inflection point of these technologies getting adopted, and we can certainly see that in the market as well. Maybe you just comment a little bit to finish off is why do you think now? Why do you think 2023 is the inflection point? Because we’ve been predicting it for a while, but I know you really believe that this is the year when it’s going to take off. Maybe you could just comment on that a little bit.

Bob Proctor:
Yeah, I think it’s both a supply phenomenon and a demand phenomenon, and that the two are just going to suddenly flip. Supply side, as you’re well aware, the marketing hype around IoT, I’m rolling back four or five years, was way ahead of the reality on the ground.

Nick Earle:
10, 12 years.

Bob Proctor:
Yeah. I mean we were the first company to have certified products using CAT-M technology in the United States on Verizon’s network, for example. And I remember they’ve been marketing the network for two years and now we’re trying to use it, and they’ve only deployed it in 10% of the cell towers, that kind of stuff. All those problems are gone. The maturity of the IoT technology is growing, and if you look at where we’re pushed location accuracy, it’s almost the evolution I described, 3 to 5 meters to 1 meter to 10 centimeters indoors and out is no longer an issue. We’ve pushed device volumes up very high, so the cost of this is all coming down the cost curve with manufacturing at scale. So on the supply side, the technology’s getting quite mature and quite robust, and so that’s great news. The technology value proposition is getting stronger, and stronger, and stronger each quarter each year.

On the demand side, what we’re seeing is I think the market moving in our direction a pretty healthy way. If I just take the food industry, for example, in the United States, even if I look back eight, nine months, technology and IoT, and tracking all this was a bit of an exploratory or nice-to-have conversation in the conversation around returnable plastic transport items. Like gosh, can you track this? What might that look like? This is a nice to know type of thing, but not a fundamental requirement if we’re thinking about moving from cardboard to plastic. Today, we’re seeing most large grocers and people are playing in the food industry saying actually this is a requirement, and we need technical specifications, and this is the direction we’re going.

So we’re seeing the market coming in this direction, and that market, I think companies on that side are waking up to there’s enormous value here in all this food waste and the technologies there. The other piece, frankly, that’s coming beyond the carrot of the business value is the stick. In the United States, there’s a thing called the Food Safety Modernization Act or Food Modernization Safety Act, I might have the acronym a little bit backwards, but from a regulatory perspective, all this is going to have to be tracked within a few years. So there’s a stick out there as well, and so the market is moving in this direction at the same time the value proposition is getting stronger and stronger. And I think we’re essentially hitting a tipping point now.

Nick Earle:
Yeah. And we’ve seen in other use cases, something you didn’t mention was the after effects of COVID is that as COVID works its way through the system, people are really focusing on efficiency, and cost reduction, and realizing that they have to leapfrog and do things a different way. Because, I was listening to the radio this morning, there’s a million and a half highly skilled jobs unfilled in North America. The people have gone, many of them have retired from the workforce, but all these things come together and then you get the first use cases of the people who are doing it, who get the commercial advantage and leapfrog, and then suddenly, as you say, it’s the tipping point. Well, Bob, we could probably talk for a long time about this. I’m sure people have loved this podcast because there’s a lot of information here, it’s very exciting. For those of you who want to know more, I mean I guess they would go to your website. Maybe want to remind people what your website address is?

Bob Proctor:
Yeah, it’s link-labs.com.

Nick Earle:
Link-labs.com, and I know there’s a lot of great information out there as to everything that we’ve talked about. And I know you’re not just a US company, are you? You’re also becoming active now in Europe as well, because this is a global problem and needs a global solution, because as you said … What was that phrase you said? I can tell you where everything is on the planet. Something like that.

Bob Proctor:
I can tell you where anything is on the planet within 10 centimeters, yeah. Provided that I’m getting my network operator coverage from Eseye. But how many countries are you guys in now?

Nick Earle:
We’re in 190 countries. There’s only three we’re not in. You use the phrase bad actors, and the reason we’re not in three is because there’s three that we classify as bad actors. So we’re in 190 countries, so we’re global as well, enabling you to do what you’re doing and it’s a great partnership. Let’s leave it there. You’ve been listening to the IoT Leaders podcast with me, your host, Nick Earle, the CEO, Eseye, and my guest this week is Bob Proctor, the founder and CEO of Link Labs. And I’m sure you found that a really, really interesting podcast and discussion. The idea of not many guests on the podcast can say things like, “I can tell you where anything is on the planet indoors or outdoors within 10 centimeters,” but in this case, you’ve really heard the details of why that claim could well be true. So thank you for listening and we’ll talk to you again on a future episode. And Bob, great to talk to you again, and thanks very much for the discussion.

Bob Proctor:
Thank you, Nick. It was great to be here today.

Outro:
Thanks for tuning in to IoT Leaders, a podcast brought to you by Eseye. Our team delivers innovative global IoT cellular connectivity solutions that just work, helping our customers deploy differentiated experiences and disrupt their markets. Learn more at eseye.com.

You’ve been listening to IoT Leaders, featuring digitization leadership on the front lines of IoT. Our vision for this podcast is to be your guide to IoT and digital disruption, helping you to plot the right route to success. We hope today’s lessons, stories, strategies, and insights have changed your vision of IoT. Let us know how we’re doing by subscribing, rating, reviewing, and recommending us. Thanks for listening. Until next time.

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