A gentle intro to design considerations for a large-scale internet-connected web of sensors
Real-time analytics- Backgrounder Many have started down the road of rolling out non-trivial Internet-of-Things platforms, and you may have, too, to some degree.
So, what happens next? Here are some things to think about – particularly in terms of handling all that data, noise and all, coming back live from your IoT network of sensors and gizmos. In short, as you’ll see, you need some way to process and condense that real-time stuff into useful analytics, as well as storing that info.
Real-time analysis is necessary because, while you can compare performance of systems and processes over weeks and months and quarters, you probably need to know a few things immediately – such as fleet vehicles breaking down, factory lines stopping, parts running out, and so on.
Back in the day these were called alerts or heuristic-based alarms. Just like network-connected embedded electronics were rebranded to IoT, remote monitoring and analysis is now called real-time analytics. And with that in mind, let’s take a closer a look at what is a fairly fiddly subject.
Size of the problem
Quantifying the state of the global IoT roll-out, Ovum earlier this year reckoned IoT projects that it classified as “small” – 500 or fewer devices or connections – account for half of European deployments. Two-thirds of enterprises have future plans for “bigger” projects, Ovum said.
Meanwhile, Vodafone in its 2017/1018 IoT Barometer last year reckonedthe number of large scale IoT projects with more than 50,000 connected devices had doubled – from a titchy base to a slightly less titchy base, from three to six per cent of those rolling out IoT.
So there’s room to grow.
IDC thinks that while the majority of IoT spending today is on hardware – modules and sensors – more than 55 per cent of spending will go on software and services by 2021.
Some of that budget will go towards security, compliance, and lifecycle management, but IDC’s prognosticators expect applications and analytics to be in the lead.
IDC explained that’s because IoT efforts most likely to be considered a “success” will be those that merge streaming analytics with machine-learning trained with data lakes, data marts and content stores, with performance boosted using dedicated accelerators or processors with built-in neural-network acceleration. The idea being that AI algorithms are used to automatically sort all the necessary info from the noise as it comes in.
And non-trivial, large-scale networks will need some level of machine intelligence to extract the needles from the haystacks.
Yes, it’s probably going to involve some form of AI
Maria Muller, AI evangelist and intelligent industry lead at integrator Avenade, supported this view on the importance of live analytics. “No longer are analytics teams thinking about their daily, weekly, or quarterly reports. The demand for data, and understanding of it, needs to happen in real time,” she said.