Episode 009 | June 15, 2021

The Internet of Things has been around for a few years now and many businesses and organizations depend on data from these systems to make critical decisions. At the same time, it is also well recognized that this data- even up to 40% of it- can be spurious, and this obviously can have a tremendously negative impact on an organizations’ decision making. But is there a way to evaluate if the sensors in a network are actually working properly and that the data generated by them are above a defined quality threshold? Join us as we speak to Dr Akshay Nambi and Ajay Manchepalli, both from Microsoft Research India, about their innovative work on making sure that IoT data is dependable and verified, truly enabling organizations to make the right decisions.

Akshay Nambi is a Senior Researcher at Microsoft Research India. His research interests lie at the intersection of Systems and Technology for Emerging Markets broadly in the areas of AI, IoT, and Edge Computing. He is particularly interested in building affordable, reliable, and scalable IoT devices to address various societal challenges. His recent projects are focused on improving data quality in low-cost IoT sensors and enhancing performance of DNNs on resource-constrained edge devices. Previously, he spent two years at Microsoft Research as a post-doctoral scholar and he has completed his PhD from the Delft University of Technology (TUDelft) in the Netherlands. 

Ajay Manchepalli, as a Research Program Manager, works with researchers across Microsoft Research India, bridging Research innovations to real-world scenarios. He received his Master’s degree in Computer Science from Temple University where he focused on Database Systems. After his Masters, Ajay spent his next 10 years shipping SQL Server products and managing their early adopter customer programs.

For more information about the Microsoft Research India click here.

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Transcript

Ajay Manchepalli: The interesting thing that we observed in all these scenarios is how the entire industry is trusting data, and using this data to make business decisions, and they don't have a reliable way to say whether the data is valid or not. That was mind boggling. You're calling data as the new oil, we are deploying these things, and we're collecting the data and making business decisions, and you're not even sure if that data that you've made your decision on is valid. To us it came as a surprise that there wasn't enough already done to solve these challenges and that in some sense was the inspiration to go figure out what it is that we can do to empower these people, because at the end of the day, your decision is only as good as the data.

[Music]

Sridhar Vedantham: Welcome to the Microsoft Research India podcast, where we explore cutting-edge research that’s impacting technology and society. I’m your host, Sridhar Vedantham.

[Music]

The Internet of Things has been around for a few years now and many businesses and organizations depend on data from these systems to make critical decisions. At the same time, it is also well recognized that this data- even up to 40% of it- can be spurious, and this obviously can have a tremendously negative impact on an organizations’ decision making. But is there a way to evaluate if the sensors in a network are actually working properly and that the data generated by them are above a defined quality threshold? Join us as we speak to Dr Akshay Nambi and Ajay Manchepalli, both from Microsoft Research India, about their innovative work on making sure that IoT data is dependable and verified, truly enabling organizations to make the right decisions.

[Music]

Sridhar Vedantham: So, Akshay and Ajay, welcome to the podcast. It's great to have you guys here.

Akshay Nambi: Good evening Sridhar. Thank you for having me here.

Ajay Manchepalli: Oh, I'm excited as well.

Sridhar Vedantham: Cool, and I'm really keen to get this underway because this is a topic that's quite interesting to everybody, you know. When we talk about things like IoT in particular, this has been a term that's been around for quite a while, for many years now and we've heard a lot about the benefits that IoT can bring to us as a society or as a community, or as people at an individual level. Now you guys have been talking about something called Dependable IoT. So, what exactly is Dependable IoT and what does it bring to the IoT space?

Ajay Manchepalli: Yeah, IoT is one area we have seen that is exponentially growing. I mean, if you look at the number of devices that are being deployed it's going into the billions and most of the industries are now relying on this data to make their business decisions. And so, when they go about doing this, we have, with our own experience, we have seen that there are a lot of challenges that comes in play when you're dealing with IoT devices. These are deployed in far off locations, remote locations and in harsh weather conditions, and all of these things can lead to reliability issues with these devices. In fact, the CTO of GE Digital mentioned that, you know, about 40% of all the data they see from these IoT devices are spurious, and even KPMG had a report saying that you know over 80% of CEOs are concerned about the quality of data that they're basing their decisions on.

And we observed that in our own deployments early on, and that's when we realized that there is, there is a fundamental requirement to ensure that the data that is being collected is actually good data, because all these decisions are being based on the data. And since data is the new oil, we are basically focusing on, ok, what is it that we can do to help these businesses know whether the data they're consuming is valid or not and that starts at the source of the truth, which is the sensors and the sensor devices. And so Akshay has built this technology that enables you to understand whether the sensors are working fine or not.

Sridhar Vedantham: So, 40% of data coming from sensors being spurious sounds a little frightening, especially when we are saying that you know businesses and other organizations base a whole lot of the decisions on the data they're getting, right?

Ajay Manchepalli: Absolutely.

Sridhar Vedantham: Akshay, was there anything you wanted to add to this?

Akshay Nambi: Yeah, so if you see, reliability and security are the two big barriers in limiting the true potential of IoT, right? And over the past few years you would have seen IoT community, including Microsoft, made significant progress to improve security aspects of IoT. However, techniques to determine data quality and sensor health remain quite limited. Like security, sensor reliability and data quality are fundamental to realize the true potential of IoT which is the focus of our project- Dependable IoT.

Sridhar Vedantham: Ok, so you know, once again, we've heard these terms like IoT for many years now. Just to kind of demonstrate what the two of you have been speaking about in terms of various aspects or various scenarios in which IoT can be deployed, could you give me a couple of examples where IoT use is widespread?

Akshay Nambi: Right, so let me give an example of air pollution monitoring. So, air pollution is a major concern worldwide, and governments are looking for ways to collect fine grained data to identify and curb pollution. So, to do this, low-cost sensors are being used to monitor pollution levels. There have been deployed in numerous places on moving vehicles to capture the pollution levels accurately. The challenge with these sensors are that these are prone to failures, mainly due to the harsh environments in which they are deployed.

For example, imagine a pollution sensor is measuring high pollution values right at a particular location. And given air pollution is such a local phenomenon, it's impossible to tell if this sensor data is an anomaly or a valid data without having any additional contextual information or sensor redundancy. And due to these reliability challenges the validity and viability of these low-cost sensors have been questioned by various users.

Sridhar Vedantham: Ok, so it sounds kind of strange to me that sensors are being deployed all over the place now and you know, frankly, we all carry sensors on ourselves, right, all the time. Our phones have multiple sensors built into them and so on. But when you talk about sensors breaking down or being faulty or not providing the right kind of data back to the users, what causes these kind of things? I mean, I know you said in the context of, say, air pollution type sensors, you know it could be harsh environments and so on, but what are other reasons for, because of which the sensors could fail or sensor data could be faulty?

Akshay Nambi: Great question, so sensors can go bad for numerous reasons, right? This could be due to sensor defect or damage. Think of a soil moisture sensor deployed in agricultural farm being run over by a tractor. Or it could be sensor drift due to wear and tear of sensing components, sensor calibration, human error and also environmental factors, like dust and humidity. And the challenge is, in all these cases, right, the sensors do not stop sending data but still continues to keep sending some data which is garbage or dirty, right? And the key challenge is it is nontrivial to detect if a remote sensor is working or faulty because of the following reasons. First a faulty sensor can mimic a non-faulty sensor data which is very hard to now distinguish. Second, to detect sensor faults, you can use sensor redundancy which becomes very expensive. Third, the cost and logistics to send a technician to figure out the fault is expensive and also very cumbersome. Finally, time series algorithms like anomaly detectors are not reliable because an anomaly need not imply it's a faulty data.

Sridhar Vedantham: So, a quick question on one of the things that you've said. When you're talking about sensor redundancy, this just means that deploy multiple sensors, so if one fails then you use the other one or do you use data from the other one. Is that what that means?

Akshay Nambi: Yeah, so sensor redundancy can be looked at both the ways. When one fails you could use the other, but it also be used to take the majority voting of multiple sensors in the same location. Going back to my air pollution example, if multiple sensors are giving very high values right, then you have high confidence in the data as opposed to thinking that is a faulty data. So that's how sensory redundancy is typically used today.

Sridhar Vedantham: OK, and there you just have to take on faith that the data that you're getting from multiple sensors is actually valid.

Akshay Nambi: Exactly, exactly. You never know that if all of them could have undergone the same fault.

Sridhar Vedantham: Right.

Ajay Manchepalli: It's interesting that when we think about how the industry tries to figure out if the sensors are working or not. There are three distinct approaches that we always observe, right? One is you have the sense of working, but you also try to use additional surrounding data. For example, let's say it's raining heavily, but your moisture sensor is indicating that the moisture level is low. That data doesn't align, right. The weather data indicates there's rains, but the moisture sensor is not giving you the right reading, so that's one way people can identify it's working or not. The other is what we just talked about, which is sensor redundancy- just to increase the number of sensors in that area and try to poll among a bunch of sensors. That also makes sense. And the third one is what typically you really can trust and that is you deploy someone out there physically, go look at the sensor and then have it tested. And if you start thinking about the scenarios we are talking about, which is remote locations, far away locations- imagine deploying sensors across the country, having to send people out and validate and things like that. There is cost associated to sending people as well as you have that sort of a down time, and so being able to, you know, remotely and reliably be able to say that the sensor is at fault, is an extremely empowering scenario. And as we look at this, it's not just sensor reliability, right? For example, if you think of a telephone, a landline, right, you have the dial tone which tells you if the phone is working or not, right? Similarly, we are trying to use certain characteristics in these sensors, that tells us if it's working or not. But the beauty of this solution is it's not just limited to being a dial tone for sensors, it is more than that. It not only tells you whether it is working or not, it can tell you if it is the sensor you intended to deploy.

I mean, think of it this way. A company could work with a vendor and procure certain class of sensors and they have an agreement for that. And when these sensors are deployed, the actual sensors that get deployed may or may not be in that class of devices, intentionally or unintentionally, right? How do you know that? If we understand the nature of the sensor, we can actually remotely identify the type of sensor that is deployed and help industries essentially figure out whether the sensor that's deployed is the sensor you intended to. So it's more than just whether the sensor is working, you can identify it, you can even figure out things like data drift. This is a pretty powerful scenario that we are going after.

Sridhar Vedantham: Right, and that's a lovely teaser for me to ask my next question. What exactly are you guys talking about and how do you do this?

Akshay Nambi: Right, so our key value proposition right is basically a simple and easy way to remotely measure and observe the health of the sensor. The core technology behind this value proposition is the ability to automatically generate a fingerprint. When I say a fingerprint, what I'm referring to is the unique electrical characteristic, exhibited by these sensors, both analog and digital. Let me give an example. So, think of analog sensors which produce continuous output signal proportional to the quantity being measured. Our key insight here is that a sensor’s voltage response right after powering down exhibits a unique characteristic, what we refer to as fall curve? This fall curve is dependent upon the sensor circuitry and the parasitic elements present in the sensor, thereby making it unique for each sensor type. So think of this as basically as fall curve acts as a reference signature for the sensor when it is working. And when the sensor goes bad, this fall curve drastically changes, and now by just comparing this fingerprint, we can tell whether a sensor is working or faulty.

Ajay Manchepalli: The interesting part about the fingerprint that Akshay just mentioned is that it is all related to the physical characteristics of the sensors, right? You have a bunch of capacitors, resistors, all of those things put together to build the actual sensor device. And each manufacturer or each sensor type or each scenario would have a different circuit and because of that, when you power down this, because of its physical characteristics, you see different signatures. So this is a unique way of being able to identify not just what type of sensor, but even based on the manufacturer, because the circuitry for that particular manufacturer will be different.

Sridhar Vedantham: So, just to clarify, when you're saying that sensors have unique fingerprints, are you talking about particular model of a sensor or a particular class of a sensor or a particular type of sensor?

Akshay Nambi: Right, great question again. So, these fingerprints are unique for that particular type of sensors. For example, take soil moisture sensor from Seed Studio, for that particular type of sensor from that manufacturer, this signature remains the same. So all you have to do is for that manufacturer and for that sensor type you collect the fingerprint once. And then you can use that to compare against the operational fingerprints. Similarly, in case of digital sensors we use current drawn as a reference fingerprint to detect whether the sensor is working or not, and the key hypothesis here behind these fingerprints, is that when a sensor accumulates damage, we believe its physical properties also change, leading to a distinct current profile compared to that of a working sensor? And that's the key property behind developing these fingerprints and one of the key aspects of these fingerprints is also that this is unaffected by the external factors like environmental changes like temperature, humidity, right. So these fingerprints are unique for each sensor type and also are independent of the environmental changes. In that way, once you collect a fingerprint that should hold good irrespective of your scenario where you are deploying the sensor.

Ajay Manchepalli: One other thing that I want to call out there is the beauty of this electrical signatures is based on the physical characteristics, right? So, it's not only when this device fails that the physical characteristics changes, and hence the signature changes, but also the beauty of this is that over time, when things degrade, that implies that the physical characteristics of that sensor or the device is also degrading, and when that happens, your electrical signature also shows that kind of degradation, and that is very powerful, because now you can actually identify or track the type of data drift that people are having. And when you observe such data drift, you can have calibration mechanisms to kind of recalibrate the data that you're getting and continue to function while you deploy people out and get it rectified, and things like that. So, it almost gives you the ability to have a planned downtime because you're not only seeing that the sensor has failed, but you are observing that the sensor will potentially fail down the line and you can take corrective actions.

Sridhar Vedantham: Right, so basically you're getting a heads up that something bad is going to happen with the sensor.

Ajay Manchepalli: Exactly.

Sridhar Vedantham: Great. And have you guys actually deployed this out in the field in real world scenarios and so on to figure out whether it works or not?

Akshay Nambi: Yeah, so this technology is already deployed in hundreds of devices in the space of agricultural farms, water monitoring and air pollution monitoring. To give you a concrete example, we are working with a company called Respirer who is using dependable IoT technology to provide reliable high fidelity pollution data to its customers and also policymakers. So, for example, Respirer today is able to provide for every data point what they measure, they are able to provide the status of the sensor, whether a sensor is working or faulty. This way users can filter out faulty or drifted data before consuming them. This has significantly increased the credibility of such low-cost sensors and the data that it is generating. And the key novelty to highlight again here is that we do this without any human intervention or redundancy. And in fact, if you think about it, we are not even looking at the sensor data. We are looking at these electrical characteristics, which is completely orthogonal to data, to determine whether the sensor is working, faulty, or drifted.

Ajay Manchepalli: The interesting part of this work is that we observed in multiple real-world scenarios that there was a real need for reliability of such sensors, and it was really impacting their function. For example, there is a team that's working on smart agriculture, and the project is called FarmBeats. And in that case, we observed that they had these sensors deployed out in the fields and out there in the farms, you have harsh conditions, and sensors could easily get damaged, and they had to actually deploy people to go and figure out what the issue is. And it became very evident and clear that it was important for us to be able to solve that challenge of helping them figure out if the sensor is working or not, and the ability to do that remotely. So that that was sort of the beginning and maybe Akshay, you can talk about the other two projects that led after that.

Akshay Nambi: Right, so another example is Respirer who is using dependable IoT technology to provide reliable, high-fidelity pollution data to its customers and policymakers. So they are now measuring the sensor status for every time they measure pollution data to determine whether the data which was measured, as from a working or a faulty or a drifted sensor. This way the users can filter out faulty or drifted data before they consume them. And this has significantly increased the credibility of low-cost sensors and the data it is measuring. To give another example, we're also working with Microsoft for Startups and Accenture for a particular NGO called. Jaljeevika, which focus on improving livelihood of small-scale fish farmers. They have a IoT device that monitors temperature, TDS, pH of water bodies to provide advisories for fish farmers. Again, since these sensors are deployed in remote locations and farmers are relying on this data and the advice being generated, it is very critical to collect reliable data. And today Jaljeevika is using dependable IoT technology to ensure the advices generated is based on reliable IoT data.

[Music]

Sridhar Vedantham: Right, so this is quite inspiring, that you've actually managed to deploy these things in, you know, real life scenarios and it's already giving benefits to the people that you're working with. You know, what always interests me with research, especially when you have research that’s deployed in the field- is there anything that came out of this that surprised you in terms of learning, in terms of outcome of the experiments that you conducted?

Akshay Nambi: Yeah, so I can give you one concrete learning, right, going back to air pollution sensors, so we have heard partners identifying these sensors going bad within just few weeks of deployment. And today they have no way to figure out what was wrong with these sensors. Using out technology, in many cases they were able to pinpoint, yes, these are faulty sensor which needed replacement right? And there was also another interesting scenario where the sensor is working well- it's just that because of dust, the sensor was showing wrong data. And we were able to diagnose that and inform the partner that all you have to do is just clean the sensor, which should bring back to the normal state as opposed to discarding that. So that was a great learning in the field what we had.

Ajay Manchepalli: The interesting thing that we observed in all these scenarios is how the entire industry is trusting data, and using this data to make business decisions, and they don't have a reliable way to say whether the data is valid or not. That was mind boggling. You're calling data as the new oil, we are deploying these things, and we're collecting the data and making business decisions, and you're not even sure if that data that you've made your decision on is valid. To us it came as a surprise that there wasn't enough already done to solve these challenges and that in some sense was the inspiration to go figure out what it is that we can do to empower these people, because at the end of the day, your decision is only as good as the data.

Sridhar Vedantham: Right. So, you know, one thing that I ask all my guests on the podcast is, you know, the kind of work that you guys do and you're talking about is truly phenomenal. And is there any way for people outside of Microsoft Research or Microsoft to actually be able to use the research that you guys have done and to be able to deploy it themselves?

Akshay Nambi: Yeah. Yeah. So all our work right is in public domain. So we have published numerous top conference papers in the areas of IoT and sensors. And all of these are easily accessible from our project page aka.ms/dependableIoT. And in fact, recently we also made our software code available through a SDK on GitHub, which we call as Verified Telemetry. So today IoT developers can now seamlessly integrate this SDK into their IoT device and get sensor status readily. We have also provided multiple samples on how do you integrate with the device, how do you use a solution sample and so on. So if you are interested, please visit aka.ms/verifiedtelemetry to access our code.

Sridhar Vedantham: Right, and it's also very nice when a research project name clearly and concisely says what it is all about. Verified Telemetry- it's a good name.

Akshay Nambi: Thank you.

Sridhar Vedantham: All right, so we're kind of coming to the end of the podcast. But before we, you know, kind of wind this thing up- what are you looking at in terms of future work? I mean, where do you go with this?

Akshay Nambi: So, till now we are mostly focused on some specific scenarios in environmental monitoring and so on, right? So, one area which we are deeply thinking is towards autonomous and safety critical systems. Imagine a faulty sensor in a self-driving vehicle or an autonomous drone, right? Or in an automated factory floor, where data from these sensors are used to take decisions without human in the loop. In such cases, bad data leads to catastrophic decisions. And recently we have explored one such safety critical sensor, which is smoke detectors. And as we all know, smoke detectors are being deployed in numerous scenarios right from hospitals to shopping malls to buildings, and the key question which we went after, right, is how do you know if your smoke detector is working or not, right? To address this, what today people do is, especially in hospitals, they do a manual routine maintenance check where a person uses a aerosol can, to trigger the smoke alarm and then turn them off in the back end.

Sridhar Vedantham: OK, that does not sound very efficient.

Akshay Nambi: Exactly, and it's also a very laborious process and significantly limits the frequency of testing? And the key challenge, unlike other sensors here, is you cannot notice failures until unless there is a fire event or smoke.

Sridhar Vedantham: Right.

Akshay Nambi: Thus it is very imperative to know whether your detector is working or not in a non-smoke condition. We have again developed a novel fingerprint which can do this and this way we can detect if a sensor is working or faulty even before a fire event occurred and alert the operators in a timely manner. So for those who are interested to understand and curious of how would you do that, please visit our webpage and access the manuscript.

Sridhar Vedantham: Yeah, so I will add links to the web page as well as to the GitHub repository in the transcript of this podcast.

Akshay Nambi: Thank you.

Sridhar Vedantham: Ajay, was there something you wanted to add to that?

Ajay Manchepalli: Yeah, in all our early deployments that we have made, we have seen that sensor fault is one of the primary issues that comes in play and that's what this has been addressing. But there are many other scenarios that come up that are very relevant and can empower the scenarios even further and that is things like, when you have the data drift or when you observe that the sensors are not connected correctly to the devices and so is some sort of sensor identification. These are some of the things that we can extend on top of what we already have. And while they are incremental changes in terms of the capability, the impact and the potential it can have for those scenarios is tremendous. And that's what keeps it exciting is that all the work that we are doing is driven by the actual needs that we are seeing out there in the field.

Sridhar Vedantham: Excellent work. And Akshay and Ajay, thank you so much once again for your time.

Akshay Nambi: Thank you Sridhar. Great having this conversation with you.

Ajay Manchepalli: Yep, thanks Sridhar. This is exciting work, and we can't wait to do more and share more with the world.

[Music]