Deploying Machine Learning to Handle Influx of IoT Data

The Internet of Things is gradually penetrating every aspect of our lives. With the growth in numbers of internet-connected sensors built into cars, planes, trains, and buildings, we can say it is everywhere. Be it smart thermostats or smart coffee makers, IoT devices are marching ahead into mainstream adoption.

But, these devices are far from perfect. Currently, there is a lot of manual input required to achieve optimal functionality — there is not a lot of intelligence built-in. You must set your alarm, tell your coffee maker when to start brewing, and manually set schedules for your thermostat, all independently and precisely.

These machines rarely communicate with each other, and you are left playing the role of master orchestrator, a labor-intensive job.

Every time the IoT sensors gather data, there has to be someone at the backend to classify the data, process them and ensure information is sent out back to the device for decision making. If the data set is massive, how could an analyst handle the influx? Driverless cars, for instance, have to make rapid decisions when on autopilot and relying on humans is completely out of the picture. Here, Machine Learning comes to play.

Tapping into that data to extract useful information is a challenge that's starting to be met using the pattern-matching abilities of machine learning. Firms are increasingly feeding data collected by Internet of Things (IoT) sensors — situated everywhere from farmers' fields to train tracks — into machine-learning models and using the resulting information to improve their business processes, products, and services.

Deploying Machine Learning + IoT Across Organizations

In this regard, one of the most significant leaders is Siemens, whose Internet of Trains project has enabled it to move from simply selling trains and infrastructure to offering a guarantee its trains will arrive on time.

Through this project, the company has embedded sensors in trains and tracks in selected locations in Spain, Russia, and Thailand, and then used the data to train machine-learning models to spot tell-tale signs that tracks or trains may be failing. Having granular insights into which parts of the rail network are most likely to fail, and when, has allowed repairs to be targeted where they are most needed — a process called 'predictive maintenance'. That, in turn, has allowed Siemens to start selling what it calls 'outcome as a service' — a guarantee that trains will arrive on-time close to 100 percent of the time.

Besides, Thyssenkrupp is one of the earliest firms to pair IoT sensor data with machine learning models, which runs 1.1 million elevators worldwide and has been feeding data collected by internet-connected sensors throughout its elevators into trained machine-learning models for several years. Such models provide real-time updates on the status of elevators and predict which are likely to fail and when, allowing the company to target maintenance where it's needed, reducing elevator outages and saving money on unnecessary servicing. Similarly, Rolls-Royce collects more than 70 trillion data points from its engines, feeding that data into machine-learning systems that predict when maintenance is required.

Market Experts' Opinions

In a recent report, IDC analysts Andrea Minonne, Marta Muñoz, Andrea Siviero say that applying artificial intelligence — the wider field of study that encompasses machine learning — to IoT data is already delivering proven benefits for firms.

"Given the huge amount of data IoT connected devices collect and analyze, AI finds fertile ground across IoT deployments and use cases, taking analytics level to uncovered insights to help lower operational costs, provide better customer service and support, and create product and service innovation," they say.

According to IDC, the most common use cases for machine learning and IoT data will be predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, in-store 'contextualized marketing' and intelligent transportation systems.

That said, companies using AI and IoT today are outliers, with many firms neither collecting large amounts of data nor using it to train machine-learning models to extract useful information.

"We're definitely still in the very early stages," says Mark Hung, research VP at analyst Gartner.

"Historically, in a lot of these use cases — in the industrial space, smart cities, in agriculture — people have either not been gathering data or gathered a large trove of data and not really acted on it," Hung says. "It's only fairly recently that people understand the value of that data and are finding out what's the best way to extract that value."

The IDC analysts agree that most firms are yet to exploit IoT data using machine learning, pointing out that "a large portion of IoT users are struggling to go beyond a mere data collection" due to a lack of analytics skills, security concerns, or simply because they don't have a "forward-looking strategic vision".

The reason machine learning is currently so prominent is because of advances over the past decade in the field of deep learning — a subset of ML. These breakthroughs were applied to areas from computer vision to speech and language recognition, allowing computers to 'see' the world around them and understand human speech at a level of accuracy not previously possible.

Machine learning uses different approaches for harnessing trainable mathematical models to analyze data, and for all the headlines ML receives, it's also only one of many different methods available for interrogating data — and not necessarily the best option.

Dan Bieler, the principal analyst at Forrester, says: "We need to recognize that AI is currently being hyped quite a bit. You need to look very carefully whether it'd generate the benefits you're looking for — whether it'd create the value that justifies the investment in machine learning."