IoT gets smarter but still needs backend analytics
3 September 2018 | 0
One way of looking at IoT deployments is this: a large array of not-particularly-sophisticated endpoints, mindlessly sending individual data points like temperature and pressure levels to either an edge device somewhere on a factory floor, or all the way out to a cloud back-end or data centre. And that is largely correct, in many cases, but it is increasingly not the whole story – IoT endpoints are getting closer and closer to the ability to do their own analysis, leading to simpler architectures and more responsive systems.
It is not the right fit for every use case, but there are types of IoT implementation that are already putting the responsibility for the customising their own metrics on the devices themselves, and more that could be a fit for such an architecture.
“Applying the lessons learned from sophisticated ML is easy enough, but some parts of the ML process are much too computationally rigorous to happen at most endpoints”
There are three main areas where letting the endpoint do its own data analysis, in whole or in part, is becoming increasingly common, namely smart cities, industrial settings and transportation.
IoT in smart cities
In smart cities smart cameras can do certain kinds of analysis right there on the device, helping planners understand pedestrian and motorised traffic patterns.
The difference between doing analytics completely on an endpoint device or partially on a device is an important one, according to Gartner research vice president Mark Hung. At the core, the analytics done by IoT implementations is about machine learning and artificial intelligence, letting systems take data provided by smart endpoints and fashion it into actionable insights about reliability, performance, and other line-of-business information automatically.
Applying the lessons learned from sophisticated ML is easy enough, even for relatively constrained devices, but some parts of the ML process are much too computationally rigorous to happen at most endpoints. This means that the endpoints themselves do not change their instructions, but that they provide information that can be used by a more powerful back-end to customise a given IoT implementation on a per-endpoint basis.
The case of video analytics for smart city applications like traffic monitoring – using a system where the cameras themselves track pedestrians and motorists, then score that data against a centrally-created AI model – is an instructive one.
Every intersection is different, so trying to push an identical rubric for making sense of different traffic patterns and volumes to the cameras monitoring every intersection is not going to work. Each intersection needs its own rubric. Yet the AI training that is needed to come up with them requires heavier computational lifting than the cameras alone can provide, so it must be done somewhere in the back end. The cameras themselves can apply the lessons learned by the AI model, but they need more powerful hardware to intelligently change the instructions they are given.
“So, to come to some preliminary analytics at the endpoint and then send that back out for further training, you have kind of a federated learning [system],” Hung said.
Another key area for endpoint-based IoT analytics is the industrial and manufacturing sector. Joe Biron is the CTO of PTC, a US software company that makes ThingWorx, an industrial IoT software platform. Biron said that PTC’s been trying to get intelligence into industrial machinery for about a decade now, with the idea being to help companies save money via predictive maintenance and other automated management and operational applications.
“Ten years ago, the state of technology for doing proactive and predictive failure detection … wasn’t exactly a life-changing kind of technology,” Biron said. It was largely a human-intelligence-driven process that relied on a technical specialist’s intimate knowledge of how the industrial components worked. Based on that knowledge, rules for detecting the parameters that predict impending failure could be hard-coded into even the “dumbest” of endpoints.
The real challenge comes when there’s no one person familiar with the critical confluence of indicators that predicts a problem in the offing. For this, you need machine learning, and more specifically, a machine-learning model that can score data inputs versus outcomes and sift out which data points are the most important to making the predictions. That is computationally expensive, according to Biron, limiting its ability to be handled on endpoints.
“Once you’ve created the model, however, now you’ve got something very lightweight to score against; now this model can be fed real-time or near real-time or microbatches of recent data, and it can be used to make statistical determinations of whether the … predicted event may happen,” he said. “The scoring of the model is cheap, computationally, but the training of the model is expensive.”
Humera Malik, CEO and founder of Canvass Analytics, said that the federation of these endpoints – and anything that’s got a digital sensor connected to it on a factory floor is an endpoint – is critically important in the industrial sector.
“It could be a shaft, it could be a bearing, it could be any of the assets – a turbine, a generator – all of these different assets that then, collectively, are running this process,” she said.
On-device IoT analytics also work well in industrial settings because there, applications of IoT tech tend not to be delay-tolerant. The time it takes for data to leave a device, negotiate a complex network topology and return in the form of corrective instructions can be too lengthy for effective device management.
IoT and smart vehicles
The third, and probably least well-realised, area where endpoint IoT analysis is getting popular is transportation. Hung notes that anything that requires autonomous navigation, be it a drone or a car or anything else, is a great candidate to be a relatively smart IoT endpoint.
Cars have been getting more and more heavily automated and computerised for years, and the advent of widespread IoT has only accelerated the process, as manufacturers build increasingly sophisticated smart safety features into modern vehicles and fleet management gains new tools for maintenance and tracking.
The increasing automation of the automobile is a great example of how this type of semi-autonomous IoT tech is supposed to work, according to Ruhollah Farchtchi, CTO of Zoomdata. “That virtuous cycle of human understanding being translated into algorithms and machine learning being deployed at the edge is a lot more of where we see the edge analytics taking shape and taking form,” he said.
IoT’s future: healthcare, energy
Looking ahead, healthcare and energy production, particularly in the oil and gas industries, are poised to become growth areas for on-device IoT analysis. Hospitals and clinics are crying out for smarter technology, such as the work being done to reduce alarm fatigue and boost interoperability in clinical environments, and having more capable computing technology built into endpoints could be a massive boon to patient care.
That is not to say there are not headaches involved, particularly where the question of machine learning comes in, according to Biron. The requisite back-end for the heavier computational lifting part of the process isn’t as easy to build into a medical facility’s architecture.
“It’s easier to see medium-scale computation happening in an environment like [a factory floor], as opposed to let’s say, a clinic, where a medical device is living – the ability to fold in high-density compute is more limited than with a manufacturing facility,” he said.
The oil and gas industry have particular advantages on that score, however, given the wealth of historical data about exploration and extraction available for use in training machine learning models.
IDG News Service