Connected Workers and the Evolution of the Industrial Workforce | SensorUp’s Steve Liang

Interested in connecting with Steve? Reach out to him on Linkedin!

About SensorUp: SensorUp’s vision is to become the fabric that interconnects the world’s IoT data empowering the connected workers of the future to apply intelligent automation for productivity, safety, quality, and job satisfaction. The core of SensorUp product is a NATO-award winning Geospatial Internet of Things and Movement Intelligence platform for complex physical operations. Our customers include some of world’s most demanding operations, including the US Department of Homeland Security, TC Energy, Husky Energy, Lockheed Martin, NASA, major mining companies, rail companies, and more.

Key Questions and Topics from this Episode:

(00:56) Intro to Steve

(01:59) Intro to SensorUp

(07:49) How was the methane emissions space being monitored?

(10:24) What’s the problem with siloed IoT systems? What should companies be aiming for instead?

(14:03) What does it mean to have connected workers? How can companies benefit?

(18:36) What advice do you have for companies starting their IoT journeys?

(23:17) How do you future-proof an IoT solution? What easy wins do you focus on to enable companies to gain ROI early on in their IoT investments?

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Transcript:

– [Announcer] You are listening to the IoT For All Media Network.

– [Ryan] Hello everyone and welcome to another episode of the IoT For All podcast on the IoT For All Media Network. I’m your host Ryan Chacon, one of the co-creators of IoT For All. Now, before we jump into this episode, please don’t forget to subscribe on your favorite podcast platform or join our newsletter at iotforall.com/newsletter to catch all the newest episodes as soon as they come out. But before we get started, does your business waste hours searching for assets like equipment or vehicles and pay full-time employees just to manually enter location and status data? You can get real-time location and status updates for assets indoors and outdoors at the lowest cost possible with Leverege’s end-to-end IoT solutions. To learn more, go to iotchangeseverything.com that’s iotchangeseverything.com. So without further ado, please enjoy this episode of the IoT For All podcast. Welcome Steve to the IoT for All show. How are things going on your end?

– [Steve] I’ve been doing great, how are you?

– [Ryan] Doing well, excited to have you. This conversation is gonna be a good one. I wanted to start off by having you give a quick introduction about yourself to our audience. Background experience, anything you think would be valuable for our audience to know about who they’re listening to.

– [Steve] Sure, my name is Steve. I’m a founder and the CTO of a company called SensorUp and then I’m also a professor in the Rogers Internet of Things Research Chair at University of Calgary. So I do research in IoT for a long time, I’m the author a several international standards on how to interconnect the world’s IoT data into a coherent system of systems. And this standard has being adopted around the world in Asia, in South Korea, Singapore, Taiwan, US Department of Homeland Security, European Union. So it’s really had some global impact, I’m very happy about it. And I see opportunity to start a company really show what’s the best practice. How to build a, IoT system of systems, interconnected systems together.

– [Ryan] Okay.

– [Steve] So I started SensorUp and SensorUp right now is backed by VC, we raised our series A last year. Yeah, it’s fun.

– [Ryan] Okay. Congrats on that. And that’s a big step. So talk a little bit more about SensorUp, you know, kind of, I’d love to hear a little bit more about the founding of the company, kinda the opportunity you saw in the market, how you got into the space originally that led to SensorUp being founded and kind of where you are now.

– Sure, so actually, Internet of Things or at the time called, Sensor Web was my master thesis topic and PHD thesis topic, right? So it’s from the research. We have very solid scientific grounding. So the idea is, instead of building siloed end to end, not into operable system, we envision the real potential of IOT is how to combine different systems together into a coherent system. like today’s web. Right, combine all different servers, internet documents, and then together into one system. So that’s our vision. So, I did research on that and then became the author of several international standards. And then I found a business opportunity, which is some users, early adopters of IoT. So imagine who are the early adopters of IoT. So IoT is not new to them, military, for example, a public safety, complex physical operations, like energy companies, right? And they have this plan about how to combine different systems together because they need that visibility. And more importantly, they need that prediction. So before that is going to happen, before wait time is going to happen, and they want to know. So I saw a business opportunity. So I started my own company, and then, I didn’t know what I get myself into, and my wife into. So in the first couple years, okay, it really is like professor tried to be entrepreneur, so it was really R&D driven. But one day actually, a company talked to me. The company is called Lockheed Martin. So they have this problem, and they heard NATO have this problem. They want to combine different systems into one. They have many different sensors, as you can imagine. So, and they want to use a standard based approach, and they found me. So they gave me a contract and then developed a prototype and I get to keep the IP.

– [Ryan] wow.

– [Steve] So I was like, doing business is easy. That’s a good customer.

– [Ryan] Right.

– [Steve] And actually at the very, you know, after that, you know, maybe two or three years is really incoming requests, right? From, for example, NASA, US Department of Homeland Security, Lockheed Martin. So they really have this vision. They want to do this, right. So we helped them. But the moment that transformed the company was 2018.

– [Ryan] Okay.

– [Steve] So we joined an accelerator called the creative destruction lab. So we have a very good technology, but a very bad business model.

– [Ryan] It’s usually one or the other, right?

– [Steve] Yeah, exactly, right? So, you know, a math professor tried to start a company, great list of customers, but doesn’t know how to do business. And then, so they put me in front of a whole bunch of investors. So after 15 minutes, somebody said, you know what Steve. You have great technology. You need money, you need some business expertise. So I have that. So I raised my seed round in 15 minutes.

– [Ryan] Yeah, wow. Wow.

– [Steve] Yeah. But then I learned actually, you know, talk about entrepreneurship, right? So there’s a big difference between technology and product. So in the past two to three years is really about, we tried to productize our technologies, make it repeatable, and then can be hardened so it can be used by real customers, right? And right we have real customers.

– [Ryan] Fantastic. So tell me about that journey you’ve kind of gone through over the last few years to make it into a product. Like what have you all learned? And where are you now? And kind of what’s the general offering to the market?

– [Steve] Okay, great. So we had technology. So the route is from how can we combine different systems into one? So create that unique value, as you know, and the audience, you might know, right? Most of the IoT system are vertical, right. Designed for a very specific application. Actually, for a good reason, right? So what we, in the past two years, it was like, okay, so who need to address this problem?

– [Ryan] Right.

– [Steve] Out the multiple system together? So they have to use that, right? So definitely not smart home. Smart home is quite homogeneous, right? So we, in the past two or three years, we decide, for us, it’s like, okay, so what are the pains our costumers really have? The acute pain, right? So for example, we have a product line is for first responders. To save lives, every second counts. And many different systems. So as a result, actually, yeah, they have this acute pain, right? We have the other customers is the rail. The category is rail. So they really, actually it’s interesting, right? The real companies, they don’t know where the rail cars are. They know the engines but don’t know where rail cars are. But the funny thing is, you know, Amazon, if you buy $10 stuff from Amazon, they know where $10 parcel is.

– [Ryan] Yeah, right. Right.

– [Steve] They don’t know where rail cars are. And because it’s a multiple silos system, legacy systems, and your special data is messy. So we found, you know, well, we fit right in. Right, so that’s great. But recently we’ve found, we try to address big problems, right. But since we found the problem fit us very, very well. So the filter for what we do is really like multiple systems and people need to care about the data rather than just the system for us. So that means more mature in the IoT journey. But also, location is very important. We are from a geospatial background. So the things that you distribute geographically, or the things that moves around.

– [Ryan] Gotcha.

– [Steve] So actually, methane emissions, or global climate change.

– [Ryan] Right.

– [Steve] We fit right in.

– [Ryan] Right.

– [Steve] Because methane is one of the biggest problem for climate change now.

– [Ryan] Okay, okay.

– [Steve] Because methane is 20 times worse than CO2.

– [Ryan] Gotcha. So before y’all got involved, how has kind of that space being monitored? Like how were methane emissions being controlled, monitored, or, you know, attempted to be reduced. And then what did you all do to kind of help solve that.

– [Steve] Great question. The problem is they’re not monitored. That’s the biggest problem.

– [Ryan] Oh, okay.

– [Steve] So actually, they don’t know what’s going on.

– [Ryan] Ah, okay. Are there any standards or regulations out there?

– [Steve] Yes.

– [Ryan] I mean, if they’re not being monitored, that means like, are they just not following the rules? Or they just, is there no real rules in place where they just don’t care?

– [Steve] There are no rules.

– [Ryan] Okay, okay.

– [Steve] No rules.

– [Ryan] Gotcha.

– [Steve] So it’s a byproduct of the production of oil and gas, right? There was no rule, but right now people trying to start to figure out the problem. And the big companies, right. The responsible companies, they are really on top of it, they try to do it. But again, it’s very new. Okay? But recently, you know, as all the climate change, which had about 40 degrees in the Rockies right now, right. We can feel it. And then also there are new regulations coming in. Okay?

– [Ryan] Okay.

– [Steve] And there’s, you know, again like IoT, there are sensors, they are capable of monitoring methane right now. Right? So there is a wild, wild west. There are many, many different sensing systems and try to solve this problem, which is quantified.

– [Ryan] Right.

– Okay so, as a result, if it does quite well, which is multiple sensors, and they need to future proof the system, because there’ll be new sensors, they are not mature sensors, right? They will have a new sensors. And a real imagine solution for methods, so imagine, you have a operation, right, on gas, it is a very big area. So you need to use satellite drones, truck mounted with sensors, and institute sensors. And at the end, you need the person, to carry a camera, to look at the leaks, to accurately quantify that. So it, in itself, is a multiple sensor solution. Could fit us very, very well. And the things that move around, right? Like drones, and trucks, and people, if it does very, very well. And also, the other big part is, there’s a missing component is a methane emissions data exchange standard.

– [Ryan] Okay.

– [Steve] So right now, for example, my company’s working with some major producers who are making a new standards so that we can really, once we collect data, right, how do we aggregate them? So, how do we share it with the world? So that’s the other missing component we are working on. So yeah, this is something we are quite excited, and it’s just doing good things. And there’s an urgent needs.

– [Ryan] Right.

– [Steve] And yeah. So we’re very happy about that part.

– [Ryan] That’s fantastic. That sounds very exciting. One thing you mentioned through talking about the company was proprietary and kind of siloed IoT systems. And I’m curious if you could elaborate a little bit more for our audience to better understand what that exactly means when you kind of say a siloed IOT system and why that’s often a challenge to work with and maybe what organizations should be aiming for instead.

– Okay. So it could look at it, right. So the typical journey for any IT systems. Okay, like the web. At the very beginning, there are multiple systems. So the proprietary network, the network talked to the nodes within the network, but they don’t talk to each other, right.

– [Ryan] Right.

– [Steve] And then there’s the internet because, you know, they tried to create a network effect, right. By interconnecting different computers, right?

– [Ryan] Right, right.

– [Steve] The internet just connect easily, it’s just a pipeline. It doesn’t connect the data. And then there’s a web. When web comes, and the web browser, and that means the information is connected. So we can mesh up information on the web and change the world totally, right?

– [Ryan] Right.

– [Steve] So, my vision about IoT, so the very beginning in 2002 where I started my master thesis is; IoT will focus this route, this decision, okay. This similar journey. At the beginning silo system, for a good reason, right. For example, there’s just so many networking protocols, just like internet. Before internet, restaurant did different networking protocols right? And then different field for deploy the sensors have different challenges, hence different protocols. But at the end, when we have enough systems, right. And people are moving their thoughts from connectivity, more like to the data itself. So when people are thinking about data itself, and then there’ll be, you know, it’s not that important about, you know, individual systems anymore.

– [Ryan] Right.

– [Steve] How to choose the network protocols anymore. It’s about how do I combine the same data from different systems together so I can have better visibility. For example, methane as an example, right? What you care is emission. The radio emission and occurrence of a mission. Do you really care about what your sensor you use?

– [Ryan] Right.

– [Steve] Not really.

– [Ryan] No.

– [Steve] In older to quantify the data accurately. Yes, you do need to know the sensor itself, but, you focus more on the observed property, which is the data, right? The reason you are collecting data.

– [Ryan] Right. Right.

– [Steve] So that’s where I believe, the future, we have to get there. And you start to see, you know, people are moving there, right. You start to see some convergence of different protocols or even platforms, right. AWS, IOT, this and that, right. MQTT SE.

– [Ryan] Of course.

– [Steve] Regular universal transport protocols, right? So, my work is really focusing on, on top of those, we are focusing on the data itself.

– [Ryan] Ah, okay.

– [Steve] And we are writing standards. For example, methane data exchange is a profile of existing standard. We wrote a standard for ISO, it’s called observations and measurement.

– [Ryan] Okay.

– [Steve] So actually, I will argue for IOT, actually, the first part, you know, you have sensors and actuators, right? So sensors give you the visibility, actually, they allow you to change the world, right? So the sensor part, actually, you don’t really care about sensor. You care about the observations, right.

– [Ryan] Okay.

– [Steve] And for the x-ray turret, you don’t really care about which brand of the robot arm you are using, or the AC you are using to reduce the temperature.

– [Ryan] Right.

– [Steve] Here is the task it’s performing.

– [Ryan] Exactly.

– [Steve] Yeah, so that’s our thesis. And of course, it he has a very nicely link into AR.

– [Ryan] Yes.

– [Steve] Yeah.

– [Ryan] Fantastic. One of the other topics you’ve mentioned briefly, and I’ve seen kind of on your site is around connected workers. And, I’d love it if you could just dive in for a second and kind of explain what connected workers means to you all, why it’s important, and also how you’ve seen COVID kind of impact IoT’s application or importance in the can connected workers space.

– [Steve] It’s their story, it’s like I share it, right. We are at the beginning of early stage of a company, really much focusing on sensors and give you the visibility. And the user persona, typically the office workers, right? You get the data together and knowing how much the safety, knowing where the train cars are and knowing how to dispatch them, this and that. But after COVID, I was locked in my basement. And I was like, thank God, I’m blessed. So I can work safely in my basement. But those frontline workers, they don’t get to enjoy the IoT, the collaboration tools we enjoy, and they use pen and paper, to binders out there, right?

– [Ryan] Right.

– [Steve] So it made me think about it. Wait a second, I was working with firefighters, right? And we are empowering firefighters to be fully connected, fully protected, and fully aware. To have the situational awareness and then even develop an AI. So to warn them when this dangerous situation happens.

– [Ryan] Right.

– [Steve] But we never thought about, actually, every frontline worker needs that. Right. Imagine, you know, they can connect to the surrounding information from different IoT systems, the digital twin data for the site they are having. And then, so we can really empower them to be more efficient and to protect them because a lot of them are working in a dangerous environment. Right? And we know that from first responders. So, last year we started to really harden our product line on the connected worker. So expand from the public safety, the military, to the frontline workers. So the ideas is really about, we improve their productivity. Okay?

– [Ryan] Okay.

– [Steve] We improve their, for example, reduce the unnecessary waiting time.

– [Ryan] That’s good, okay.

– [Steve] Everybody hates waiting time. And, the otherwise job quality, which means, allow them to do things right the first time. To help them to do that, right. So, the second part, but most important part for us, we very much focus on is job satisfaction. So we very much focused on user experience. How can we empower them with the IoT data, the situational awareness, so that they can become happy about their jobs?

– [Ryan] Right.

– [Steve] Rather then hurting the cats, you know, miscommunication, wait there for somebody to come to open the gate for them, those types of things. Right. And that’s a COVID story, is bringing us to really focus on that.

– [Ryan] Did you have contact with a lot of frontline workers and people in this space while you were kind of exploring different solutions that were available that would help do, you know, kind of what you’ve been already talking about.

– [Steve] Definitely. We need to work with them closely. So to make sure the user experience is great. Right? So as a result, actually, we chose the methane emission as out beachhead market, because part of the, you know, you might think about methane as a whole bunch of sensors, you know, flying around, this and that. Or sets it up sensor repeatedly, right?

– [Ryan] Right, right.

– [Steve] At the end of day, to have a very accurate reading, you need to send a frontline workers, to the remote site, carry a camera, and then to duty quantification. So, in itself, is a connected worker play.

– [Ryan] Right.

– [Steve] Okay? So we are very much focusing on that, how to empower them to do the leak detection and repair. So there’s a term called Eldar. So how to empower them to do that. And then empower them to closely, or seamlessly, work with the office worker, the emission engineers.

– [Ryan] Right, right.

– [Steve] So that they can, yeah, just do things more efficiently. And safety is very important as well. And also the regulator, they also require the producers, energy companies, to record, for example, activities in those sites. So again, we don’t want them to feel those forms. What did I do, blah, blah, blah, the time, enter, blah, blah, blah. They arrive at the site, we record, they click arrive. The weather has been reported, precipitation reported, humidity reported. And then the activity will be automatically recorded because the GPS, right.

– [Ryan] Right.

– [Steve] And then when they finish the next day, I’m finished, I’m done, then report is generated for them.

– [Ryan] Fantastic. That’s awesome. Now, I wanted to take a step back and kind of talk a little bit more in a broad sense for our audience out there, about just getting started on their IoT journey. Through your interactions with different companies and different industries, what have you seen, or I guess, what advice would you have for companies looking to get started on their I0T journey? How should they start? How should they be thinking about it? How should they be planning? You know, what should they focus on? And as they’re venturing down this path of deciding if, and maybe how, IoT can benefit their organization, or the organization of their customers, perhaps.

– Great question. And we help a lot of companies to go through this journey, right? So we are a data company, or AI company.

– [Ryan] Right.

– [Steve] And so I always encourage my customers to think about data.

– [Ryan] Okay.

– [Steve] Network is important, yes. The power consumption to enterprise is important, but at the end of the day is your data. But in addition to data, right? So there are two kinds of data. There’s one called observations, right?

– [Ryan] Okay.

– [Steve] To give you the visibility, but then, what you really want is a prediction.

– [Ryan] Right.

– [Steve] Before bad thing’s going to happen, you want to know. Before there is a heart attack on the first responders, they want to save them. Right? And before there is a incident, right? For some oil and gas sites, right? And then you want to stop people to go there. So on and so forth. So before we need detection, right. Before the leak’s going to happen, you want to dispatch troops to fix them. That’s more important. So we have a framework. It’s called AI IoT canvas. And it’s based on a book, by the way, for anybody interested in reading, there’s a book called Prediction Machines. So it’s, look at AI from a economy perspective, or business perspective, right? So it’s not for techies. And in itself, it has a AI canvas. So we added the IoT flavor to it. It’s called AI IoT canvas.

– [Ryan] Okay.

– [Steve] So it basically, what it does is it’s a framework that allows you to break down your workflows. For every business, you have the workflows, right. So, break down the workflows into different tasks. And then you try to extract the prediction task out of it.

– [Ryan] Okay.

– [Steve] And then try to see what input’s required, to input basically from IoT, right, the sensors.

– [Ryan] Sure, sure.

– [Steve] And what AI to apply so that you can get that prediction. And you replace humans too, with AI, to make that prediction. But eventually, you use that prediction to empower the human to make a judgment.

– [Ryan] Right.

– [Steve] Okay. So judgment, definition of judgment is, the risk of not doing it.

– [Ryan] Sure, okay.

– [Steve] Let me give you an example. Leak detection and repair for methane, right? So what are the inputs? The different methane sensor. So it’s not only one, multiple. What’s the prediction you want? You want to fix the leaks before they happen. Right? The first step is the monitoring, but we want to do better. So, fix it before it leaks, okay? And then that’s prediction. Once you have the data, you run the AI models, and then different models, and then you kept that predictions, and you present to the emission engineer, and engineer makes that judgment. Okay, how do I, because I have a limited time and budget, how do I dispatch my crews to fix them, to repair them, to monitor them, so that I have the best, you know, the lower the cost and to stop the leaks. Okay.

– [Ryan] Right.

– [Steve] And then you have, because IoT gives you the continuous monitoring. So you have the outcomes, right. Once you have the tasks, right. You have a judgment, you deploy your tasks and you have the outcomes, which is, do I really reduce my emission or not? Do I improve my safety records or not?

– [Ryan] Right.

– [Steve] And those will be fed back into the AI to make your next prediction better.

– [Ryan] Okay.

– [Steve] We use this framework. And then, so this is one prediction task, right, for this. But we just run the framework, right. Like canvas, you have different canvases, right. And then you identify which one would be most disruptive or transformative.

– [Ryan] Okay. Right, right.

– [Steve] And the framework I took, typically I told my customer, is like, if you see a prediction task can really transform by AI IOT. And you think about what if my competitor has this, I’ll be doomed, they’ll eat my lunch. That’s your priority.

– [Ryan] Fantastic. Yeah, that’s a good way to put it. You know, we usually talk about it in the realm of the individual company. We don’t always look at it from the competitor standpoint as you know, kind of that fear. So that makes a ton of sense. One last question I had for you. So, when we think about starting an IOT journey, oftentimes a lot of companies have questions about the future proof of technology, since IOT has lots of different technologies involved, different components, so forth. I wanted to kind of get a sense from you of, when you’re working with organizations, how do you answer that question on how future-proof an IoT solution is, as well as, what type of, kind of quick win ROIs do you focus on for companies to see the business impact early on so they invest the time, the money, and the resources into scaling an IoT solution, which is kind of where everybody wins.

– [Steve] Okay. Great question. So about future proof, right? So there’s one thing. So, this jet fighter, it’s a snowbird. Yeah, it’s a Canadian, there’s this air show right now.

– [Ryan] Oh, gotcha.

– [Steve] Got it, okay. So, for future proof, so not that every organization needs feature proof stuff. So who needs future proof, right? Isn’t that number one. So ask about what’s my life cycle for my technology, or the initiative, right? If I need future proof, and my typically recommendation is, find something which is open standard, open source. Okay. Open standard, not necessarily open source, open standard. So as a result, you see military their lifecycle for their stuff, right, the assets, is like decades. So they need to depend on something that lasts longer and not controlled by only one party.

– [Ryan] Okay.

– [Steve] So try to use open standard, right. So protocol, MQTT, why is it popular? It’s open standard, right. If it, you know, good implementations.

– [Ryan] Yup, yup.

– [Steve] And so that’s my recommendation, but it’s more complicated than that, right. But is a good place to start.

– [Ryan] Right.

– [Steve] But don’t get locked in. So, number two, for the priorities, right, I think I kind of insert with the AI IOT campus framework. Right, you have different canvases, right? You do this exercise, you break your workflows into tasks and then you have different canvases.

– [Ryan] Right.

– [Steve] And then you start to see through the priorities.

– [Ryan] Right.

– [Steve] So build inventory first and then prioritize them and review them regularly.

– [Ryan] Makes sense.

– [Steve] Right?

– [Ryan] Right.

– [Steve] So the other way to do it is like, for the more mature companies, organizations, right, oil and gas, energy, military, they already have many sensors, right. So build the inventory of inputs and go through this exercise about the IOT and AI canvases, right. And then, you know, prioritize stuff, right. So in this case, which is very, very quick because you leverage your existing data rather than deployed a network, capital expenditure, this and that, right.

– [Ryan] Right, right, right.

– [Steve] Your data has more value than, you know, actually.

– [Ryan] It does, yeah. Makes a lot of sense. Well, this conversation has been fantastic. I appreciate the way you’ve broken down, kind of everything we’ve talked about. It’s great for our audience to kind of get insights from somebody with, I don’t know, incredible level of experience and expertise in this space. I mean, the fact that you started out through a thesis and have evolved it into a company is amazing. Raising money on the idea of the technology. Just super impressive stuff. So, so thank you so much for your time. The last thing I wanted to ask you is if our audience wants to learn more, has questions, follow up, all that kind of good stuff, what’s the best way to reach out?

– [Steve] So, go to sensorup.com and then also, I think some of you guys might be interested in this IoT and AI canvas.

– [Ryan] Yes.

– [Steve] Reach out to us, we can go over it with you and help you through the journey.

– [Ryan] Fantastic. Awesome. Well, thank you so much Steve for your time. This has been a fantastic conversation and I really appreciate it.

– [Steve] Thank you very much.

– [Announcer] Alright, everyone. Thanks again for joining us this week on the IoT For All podcasts, I hope you enjoyed this episode. And if you did, please leave us a rating or review and be sure to subscribe to our podcast on whichever platform you’re listening to us on. Also, if you have a guest you’d like to see on the show, please drop us a note at and we’ll do everything we can to get them as a featured guest. Other than that, thanks again for listening. And we’ll see you next time.

Source: https://www.iotforall.com/podcasts/e138-connected-workers-industrial-workforce